Methods and systems for automated clinical study simulation

Abstract

The present disclosure provides a method of automated clinical study simulation. Further, in some embodiments, the method simulates clinical studies via conversational interactions with digital twin patients. Further, the method includes receiving profile data from one or more client devices, generating a digital twin data based on the profile data, generating one or more user inputs for an AI model using the digital twin data, and obtaining one or more response data from the AI model. Further, the AI model may be configured to generate the one or more response data for the one or more user inputs. Further, the method includes analyzing the one or more user inputs and the one or more response data, generating one or more result data based on the analyzing, and transmitting the one or more result data to the one or more client devices.

Description

[0001] METHODS AND SYSTEMS FOR AUTOMATED CLINICAL STUDY SIMULATION

[0002] REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 685,450, titled “METHODS AND SYSTEMS FOR FACILITATING AUTOMATED CLINICAL STUDY SIMULATION”, filed on Aug. 21, 2024, which is incorporated by reference herein in its entirety.

[0004] FIELD OF DISCLOSURE

[0005] The present disclosure relates to the field of data processing. More specifically, the present disclosure relates to systems and methods.

[0006] BACKGROUND

[0007] The present disclosure relates to the field of computer-implemented healthcare research operations, with particular focus on generation of clinical study documentation derived from conversational interaction in a networked computing environment. The field is important because research sponsors, investigators, and oversight bodies rely on accurate, consistent, and timely documentation to demonstrate adherence to study protocol, protect participant safety, and advance scientific understanding while operating at scale across distributed sites.

[0008] A desirable objective in the field is to facilitate protocol-compliant clinical study reporting based on conversational interaction in a manner that is consistent, reproducible, auditable, privacy-preserving, and efficient under real-world deployment constraints typical of software-as-a-service platforms.

[0009] However, achieving the objective is challenged by several factors. Dynamic conversational interaction may deviate from prescriptive protocol logic, creating gaps, off-protocol content, or inconsistent sequencing that complicates downstream documentation. Lack of fine-grained, machine- auditable metrics and provenance may hinder explainability, quality assurance, and regulatory review, especially when documentation must reflect specific milestones, eligibility checks, and decision rationales. Protection of sensitive data embedded in dialog content raises concerns about identification, linkage risk, and secure processing within multi-tenant cloud environments, particularly when multiple organizations must collaborate without centralizing raw data. Safety and risk management remain difficult when conversational outputs may contain unsupported clinical statements or speculative advice that must be prevented from entering formal records.

[0010] Further, assembling documentation that conforms to a complex protocol-defined format is error-prone when structural validation, unit and range checks, and cross-field dependencies are handled manually or inconsistently. Fairness oversight presents additional challenges because subtle demographic or contextual disparities may affect error profiles in ways that are hard to detect without systematic analysis. Practical deployment also faces latency and throughput constraints: deep validation and review may introduce unpredictable delays that undermine service-level commitments in an online setting.

[0011] Additional operational risks include the absence of tamper-evident activity records that support forensic reconstruction, exposure to prompt-level attacks that attempt to elicit off-policy content, and scarcity of shareable dialog corpora for benchmarking under privacy constraints. Sensitivity analysis to isolate the effect of specific patient or scenario factors is rarely available, leaving stakeholders without a clear view of causal drivers of success or failure. Confidence estimation for generated content is also limited, reducing the ability to gate or suppress low-confidence statements before propagating into formal study records. Current conversational Al solutions in healthcare fall into two extremes: rigid rule -based decision trees that cannot adapt to patient variability, or unconstrained machine learning models that produce unpredictable outputs without deterministic control or prior simulation.

[0012] Therefore, there is a need for improved systems and methods for automated clinical study simulation and validation of Al model performance that can overcome one or more of the preceding problems.

[0013] SUMMARY OF DISCLOSURE

[0014] This summary is provided to introduce a selection of concepts in a simplified form, that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this summary intended to be used to limit the claimed subject matter’s scope.

[0015] The present disclosure provides a method of automated clinical study simulation. Further, the method may include receiving, using a communication device, one or more profile data from one or more client devices. Further, the one or more profile data may be associated with one or more users. Further, the method may include generating, using a processing device, a digital twin data based on the one or more profile data. Further, the digital twin data corresponds to a digital representation of the one or more users. Further, the method may include generating, using the processing device, one or more user inputs for an Al model using the digital twin data. Further, the method may include obtaining, using the processing device, one or more response data from the Al model. Further, the Al model may be configured to generate the one or more response data for the one or more user inputs. Further, the method may include analyzing, using the processing device, the one or more user inputs and the one or more response data. Further, the method may include generating, using the processing device, one or more result data based on the analyzing. Further, the one or more result data includes a result associated with the validation of the performance of the Al model. Further, the method may include transmitting, using the communication device, the one or more result data to the one or more client devices.

[0016] The present disclosure provides a system for automated clinical study simulation. Further, the system may include a communication device. Further, the communication device may be configured for receiving one or more profile data from one or more client devices. Further, the one or more profile data may be associated with one or more users. Further, the communication device may be configured for transmitting one or more result data to the one or more client devices. Further, the system may include a processing device communicatively coupled with the communication device. Further, the processing device may be configured for generating a digital twin data based on the one or more profile data. Further, the digital twin data corresponds to a digital representation of the one or more users. Further, the processing device may be configured for generating one or more user inputs for an Al model using the digital twin data. Further, the processing device may be configured for obtaining one or more response data from the Al model. Further, the Al model may be configured to generate the one or more response data for the one or more user inputs. Further, the processing device may be configured for analyzing the one or more user inputs and the one or more response data. Further, the processing device may be configured for generating the one or more result data based on the analyzing. Further, the one or more result data includes a result associated with the validation of the performance of the Al model.

[0017] Both the foregoing summary and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing summary and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.

[0018] BRIEF DESCRIPTIONS OF DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the applicants. The applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

[0019] Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure.

[0020] Fig. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure.

[0021] Fig. 2 is a block diagram of a computing device 200 for implementing the methods disclosed herein, in accordance with some embodiments.

[0022] Fig. 3A illustrates a flowchart of a method 300 of automated clinical study simulation, in accordance with some embodiments.

[0023] Fig. 3B illustrates a continuation of the flowchart of the method 300 of automated clinical study simulation, in accordance with some embodiments.

[0024] Fig. 4 illustrates a flowchart of a method 400 of automated clinical study simulation including generating, using the processing device 704, at least one rule for generating the at least one user input based determining of the at least one characteristic, in accordance with some embodiments.

[0025] Fig. 5 illustrates a flowchart of a method 500 of automated clinical study simulation including determining, using the processing device 704, a stability metric of the digital twin, in accordance with some embodiments. Fig. 6 illustrates a flowchart of a method 600 of automated clinical study simulation including encrypting, using the processing device 704, at least one of the at least one user input and the at least one response data, in accordance with some embodiments.

[0026] Fig. 7 illustrates a block diagram of a system 700 for automated clinical study simulation, in accordance with some embodiments.

[0027] Fig. 8 illustrates a flowchart of a method 800 of automated clinical study simulation including repeating, using the processing device 704, the generating of the at least one user input, in accordance with some embodiments.

[0028] Fig. 9 is a block diagram of a system 900 for facilitating simulating automated clinical study, in accordance with some embodiments.

[0029] Fig. 10 is a flow chart of a method 1000 for facilitating simulating automated clinical study, in accordance with some embodiments.

[0030] DETAILED DESCRIPTION OF DISCLOSURE

[0031] As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the abovedisclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure. Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim limitation found herein and / or issuing here from that does not explicitly appear in the claim itself.

[0032] Thus, for example, any sequence(s) and / or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present disclosure. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.

[0033] Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein — as understood by the ordinary artisan based on the contextual use of such term — differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

[0034] Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.” The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the claims found herein and / or issuing here from. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.

[0035] The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of the disclosed use cases, embodiments of the present disclosure are not limited to use only in this context.

[0036] In general, the method disclosed herein may be performed by one or more computing devices. For example, in some embodiments, the method may be performed by a server computer in communication with one or more client devices over a communication network such as, for example, the Internet. In some other embodiments, the method may be performed by one or more of at least one server computer, at least one client device, at least one network device, at least one sensor and at least one actuator. Examples of the one or more client devices and / or the server computer may include, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a portable electronic device, a wearable computer, a smart phone, an Internet of Things (loT) device, a smart electrical appliance, a video game console, a rack server, a super-computer, a mainframe computer, mini-computer, micro-computer, a storage server, an application server (e.g. a mail server, a web server, a real-time communication server, an FTP server, a virtual server, a proxy server, a DNS server etc.), a quantum computer, and so on. Further, one or more client devices and / or the server computer may be configured for executing a software application such as, for example, but not limited to, an operating system (e.g. Windows, Mac OS, Unix, Linux, Android, etc.) in order to provide a user interface (e.g. GUI, touch-screen based interface, voice based interface, gesture based interface etc.) for use by the one or more users and / or a network interface for communicating with other devices over a communication network. Accordingly, the server computer may include a processing device configured for performing data processing tasks such as, for example, but not limited to, analyzing, identifying, determining, generating, transforming, calculating, computing, compressing, decompressing, encrypting, decrypting, scrambling, splitting, merging, interpolating, extrapolating, redacting, anonymizing, encoding and decoding. Further, the server computer may include a communication device configured for communicating with one or more external devices. The one or more external devices may include, for example, but are not limited to, a client device, a third party database, public database, a private database and so on. Further, the communication device may be configured for communicating with the one or more external devices over one or more communication channels. Further, the one or more communication channels may include a wireless communication channel and / or a wired communication channel. Accordingly, the communication device may be configured for performing one or more of transmitting and receiving of information in electronic form. Further, the server computer may include a storage device configured for performing data storage and / or data retrieval operations. In general, the storage device may be configured for providing reliable storage of digital information. Accordingly, in some embodiments, the storage device may be based on technologies such as, but not limited to, data compression, data backup, data redundancy, deduplication, error correction, data finger-printing, role based access control, and so on.

[0037] Further, one or more steps of the method disclosed herein may be initiated, maintained, controlled and / or terminated based on a control input received from one or more devices operated by one or more users such as, for example, but not limited to, an end user, an admin, a service provider, a service consumer, an agent, a broker and a representative thereof. Further, the user as defined herein may refer to a human, an animal or an artificially intelligent being in any state of existence, unless stated otherwise, elsewhere in the present disclosure. Further, in some embodiments, the one or more users may be required to successfully perform authentication in order for the control input to be effective. In general, a user of the one or more users may perform authentication based on the possession of a secret human readable secret data (e.g. username, password, passphrase, PIN, secret question, secret answer etc.) and / or possession of a machine readable secret data (e.g. encryption key, decryption key, bar codes, etc.) and / or or possession of one or more embodied characteristics unique to the user (e.g. biometric variables such as, but not limited to, fingerprint, palm-print, voice characteristics, behavioral characteristics, facial features, iris pattern, heart rate variability, evoked potentials, brain waves, and so on) and / or possession of a unique device (e.g. a device with a unique physical and / or chemical and / or biological characteristic, a hardware device with a unique serial number, a network device with a unique IP / MAC address, a telephone with a unique phone number, a smartcard with an authentication token stored thereupon, etc.). Accordingly, the one or more steps of the method may include communicating (e.g. transmitting and / or receiving) with one or more sensor devices and / or one or more actuators in order to perform authentication. For example, the one or more steps may include receiving, using the communication device, the secret human readable data from an input device such as, for example, a keyboard, a keypad, a touch-screen, a microphone, a camera and so on. Likewise, the one or more steps may include receiving, using the communication device, the one or more embodied characteristics from one or more biometric sensors.

[0038] Further, one or more steps of the method may be automatically initiated, maintained and / or terminated based on one or more predefined conditions. In an instance, the one or more predefined conditions may be based on one or more contextual variables. In general, the one or more contextual variables may represent a condition relevant to the performance of the one or more steps of the method. The one or more contextual variables may include, for example, but are not limited to, location, time, identity of a user associated with a device (e.g. the server computer, a client device etc.) corresponding to the performance of the one or more steps, environmental variables (e.g. temperature, humidity, pressure, wind speed, lighting, sound, etc.) associated with a device corresponding to the performance of the one or more steps, physical state and / or physiological state and / or psychological state of the user, physical state (e.g. motion, direction of motion, orientation, speed, velocity, acceleration, trajectory, etc.) of the device corresponding to the performance of the one or more steps and / or semantic content of data associated with the one or more users. Accordingly, the one or more steps may include communicating with one or more sensors and / or one or more actuators associated with the one or more contextual variables. For example, the one or more sensors may include, but are not limited to, a timing device (e.g. a real-time clock), a location sensor (e.g. a GPS receiver, a GLONASS receiver, an indoor location sensor etc.), a biometric sensor (e.g. a fingerprint sensor), an environmental variable sensor (e.g. temperature sensor, humidity sensor, pressure sensor, etc.) and a device state sensor (e.g. a power sensor, a voltage / current sensor, a switch-state sensor, a usage sensor, etc. associated with the device corresponding to performance of the or more steps).

[0039] Further, the one or more steps of the method may be performed one or more number of times. Additionally, the one or more steps may be performed in any order other than as exemplarily disclosed herein, unless explicitly stated otherwise, elsewhere in the present disclosure. Further, two or more steps of the one or more steps may, in some embodiments, be simultaneously performed, at least in part. Further, in some embodiments, there may be one or more time gaps between performance of any two steps of the one or more steps.

[0040] Further, in some embodiments, the one or more predefined conditions may be specified by the one or more users. Accordingly, the one or more steps may include receiving, using the communication device, the one or more predefined conditions from one or more and devices operated by the one or more users. Further, the one or more predefined conditions may be stored in the storage device. Alternatively, and / or additionally, in some embodiments, the one or more predefined conditions may be automatically determined, using the processing device, based on historical data corresponding to performance of the one or more steps. For example, the historical data may be collected, using the storage device, from a plurality of instances of performance of the method. Such historical data may include performance actions (e.g. initiating, maintaining, interrupting, terminating, etc.) of the one or more steps and / or the one or more contextual variables associated therewith. Further, machine learning may be performed on the historical data in order to determine the one or more predefined conditions. For instance, machine learning on the historical data may determine a correlation between one or more contextual variables and performance of the one or more steps of the method. Accordingly, the one or more predefined conditions may be generated, using the processing device, based on the correlation.

[0041] Further, one or more steps of the method may be performed at one or more spatial locations. For instance, the method may be performed by a plurality of devices interconnected through a communication network. Accordingly, in an example, one or more steps of the method may be performed by a server computer. Similarly, one or more steps of the method may be performed by a client computer. Likewise, one or more steps of the method may be performed by an intermediate entity such as, for example, a proxy server. For instance, one or more steps of the method may be performed in a distributed fashion across the plurality of devices in order to meet one or more objectives. For example, one objective may be to provide load balancing between two or more devices. Another objective may be to restrict a location of one or more of an input data, an output data and any intermediate data there between corresponding to one or more steps of the method. For example, in a client-server environment, sensitive data corresponding to a user may not be allowed to be transmitted to the server computer. Accordingly, one or more steps of the method operating on the sensitive data and / or a derivative thereof may be performed at the client device.

[0042] Overview:

[0043] The present disclosure describes methods and systems for facilitating simulating automated clinical study. Further, the disclosed system may be configured for simulating clinical studies using conversational Al, designed to facilitate the rigorous testing and validation of Al-driven health diagnostics and therapeutic solutions. The disclosed system employs a multi-stage simulation process, encompassing deterministic and non- deterministic interactions, to emulate a wide range of patient interactions and clinical scenarios. By leveraging digital twins, synthetic personas, and advanced natural language processing techniques, the system may conduct extensive, repeatable, and scalable clinical trials without the need for human subjects. The approach ensures the safety, efficacy, and regulatory compliance of conversational Al applications in healthcare. While the primary focus is on healthcare applications, the system may be adapted for use in other industries requiring robust Al validation and testing, such as finance, customer service, and education. The system may include an integrated reporting engine to generate comprehensive study reports for publication and regulatory submission.

[0044] As used herein, 'interaction stage' refers to a defined level of conversational complexity (e.g., deterministic, semi-deterministic, or non-deterministic). 'Stability metric' means a quantitative measure of digital twin behavioral consistency across repeated simulations. 'Bias indicator' refers to any measurable factor for assessing performance disparities. 'Validation request' means an invocation specifying study parameters and interaction stages to execute.

[0045] The use of artificial intelligence (Al) in healthcare has grown significantly, promising improvements in diagnostic accuracy, personalized treatment, and patient outcomes. However, integrating Al into clinical practice poses challenges, particularly regarding safety, efficacy, and regulatory compliance. Traditional clinical trials are resourceintensive and time-consuming, often delaying the deployment of innovative technologies. Conversational Al systems, which use natural language processing to interact with patients, are particularly promising in mental health care for tasks such as pre-diagnosis, therapeutic support, and crisis intervention. However, the development and validation of the systems require rigorous testing to ensure providing accurate, reliable, and safe interactions. Traditional clinical trials may not be sufficient to address the unique challenges posed by conversational Al, such as variability in patient interactions and the need for continuous learning and adaptation.

[0046] Furthermore, the regulatory landscape for Al in healthcare is complex, with stringent requirements for data privacy, security, and patient safety (e.g., Health Insurance Portability and Accountability Act (HIPAA), Personal Information Protection and Electronic Documents Act (PIPED A), and General Data Protection Regulation (GDPR). Existing methods for Al validation often lack the flexibility to adapt to the evolving standards, creating a need for a robust, scalable solution that can ensure compliance while facilitating rapid innovation. The present disclosure addresses the challenges by providing a comprehensive framework for simulating clinical studies using conversational Al. The framework allows for the testing of both deterministic and non- deterministic conversational models across various stages of interaction, from initial patient intake to complex diagnostic and therapeutic processes. By leveraging digital twins and advanced simulation techniques, the system enables thorough validation of Al systems in a controlled, ethical, and efficient manner, paving the way for safer and more effective Al applications in healthcare and beyond.

[0047] Additionally, the disclosed system relates to the field of artificial intelligence and clinical study simulations, specifically focusing on the development and validation of conversational Al systems within healthcare. The disclosed system encompasses a framework for automating clinical trials through simulated interactions, employing both deterministic and non-deterministic conversational models. While the primary application of the disclosed system is in healthcare, enabling robust testing of Al-driven diagnostic and therapeutic tools, the disclosed system may be also applicable to other industries such as finance, customer service, and education, where Al validation and safety are critical.

[0048] Further, the framework allows for the testing of both deterministic and non- deterministic conversational models across various stages of interaction, from initial patient intake to complex diagnostic and therapeutic processes. By leveraging digital twins and advanced simulation techniques, the disclosed system enables thorough validation of Al systems in a controlled, ethical, and efficient manner, paving the way for safer and more effective Al applications in healthcare and beyond.

[0049] The disclosed system provides the comprehensive framework designed to simulate clinical studies for validating conversational Al systems, specifically those utilized in healthcare. The disclosed system addresses the unique challenges of developing, testing, and deploying conversational Al by enabling detailed simulation of patient interactions across a spectrum of scenarios, from deterministic question-answer exchanges to complex, dynamic conversations driven by large language models (LLMs).

[0050] Key aspects of the disclosed system include:

[0051] 1. Simulation Framework: A robust simulation platform capable of creating digital twins, which are virtual representations of patients with diverse characteristics and conditions.

[0052] The ability to simulate numerous interactions, allowing for extensive testing and validation without the need for immediate human trials. f Interaction:

[0053] The framework supports various stages of interaction, ranging from simple, deterministic workflows to highly complex, non-deterministic conversational flows.

[0054] Each stage is designed to test specific aspects of the conversational Al’s performance, ensuring thorough validation across different levels of complexity. ion with Clinical Studies:

[0055] The system includes methodologies for integrating simulation results with clinical study protocols, facilitating the transition from simulated to real- world validation.

[0056] The system supports continuous feedback loops, enabling the Al to learn and improve from simulated interactions before being tested in actual clinical environments. ory Compliance:

[0057] Built-in compliance features to meet stringent regulatory requirements, including HIPAA, PIPEDA, and GDPR.

[0058] Data privacy and security measures, such as the segregation of personally identifiable information (PII) from clinical data and the use of anonymized data for ongoing improvement. Machine Learning Integration:

[0059] Advanced Al and machine learning capabilities to dynamically adjust and optimize conversational flows based on real-time feedback.

[0060] A reporting engine to generate detailed analytics and insights, supporting clinical study publication and continuous improvement of Al models.ity Across Industries: While primarily designed for healthcare, the framework may be adapted for use in other industries requiring rigorous Al validation, such as finance, customer service, and education.

[0061] The adaptability is achieved through configurable modules that may be tailored to specific industry needs.

[0062] The system offers a scalable, efficient, and ethical approach to developing and validating conversational Al systems, ensuring the system are safe, reliable, and ready for deployment in critical applications. By bridging the gap between simulation and real- world testing, the framework accelerates the innovation cycle while maintaining the highest standards of patient safety and data integrity.

[0063] Additionally, the disclosed system may provide a robust simulation platform capable of creating digital twins, which are virtual representations of patients with diverse characteristics and conditions. Further, the disclosed system may be configured to simulate numerous interactions, allowing for extensive testing and validation without the need for immediate human trials.

[0064] The framework supports various stages of interaction, ranging from simple, deterministic workflows to highly complex, non-deterministic conversational flows. Each stage may be designed to test specific aspects of the conversational Al’s performance, ensuring thorough validation across different levels of complexity.

[0065] The disclosed system provides methodologies for integrating simulation results with clinical study protocols, facilitating the transition from simulated to real-world validation. Additionally, the disclosed system supports continuous feedback loops, enabling the Al to learn and improve from simulated interactions before being tested in actual clinical environments.

[0066] Additionally, the disclosed system may provide built-in compliance features to meet stringent regulatory requirements, including HIPAA, PIPEDA, and GDPR. Further, the disclosed system incorporates privacy and security measures — such as data masking, pseudonymization, and role-based access control — by design, such as the segregation of personally identifiable information (PII) from clinical data and the use of anonymized data for ongoing improvement. Additionally, the disclosed system may be based on Advanced Al and machine learning capabilities to dynamically adjust and optimize conversational flows based on real-time feedback. Additionally, the disclosed system may include a reporting engine to generate detailed analytics and insights, supporting clinical study publication and continuous improvement of Al models. While primarily designed for healthcare, the framework may be adapted for use in other industries requiring rigorous Al validation, such as finance, customer service, and education. The adaptability is achieved through configurable modules that may be tailored to specific industry needs. Further, the disclosed system offers a scalable, efficient, and ethical approach to developing and validating conversational Al systems, ensuring the system is safe, reliable, and ready for deployment in critical applications. By bridging the gap between simulation and real-world testing, the framework accelerates the innovation cycle while maintaining the highest standards of patient safety and data integrity.

[0067] 1. Simulation Framework

[0068] Digital Twins:

[0069] Detailed Representations: Digital twins are detailed representations of patients that may exist in both virtual and physical forms. The twins include specific medical histories, symptoms, and behavioral patterns. Virtual twins are computer-generated models that simulate patient interactions and conditions. Physical twins, such as androids, are robots or devices designed to mimic patient behaviors and responses physically.

[0070] Virtual Twins: Utilize advanced algorithms and data integration to create comprehensive virtual models of patients. The models are capable of simulating a wide range of medical conditions and interactions.

[0071] Physical Twins: Include androids or other physical devices that are designed to replicate patient behaviors, symptoms, and responses. The physical twins may be used in conjunction with virtual models to provide a more holistic testing and validation environment.

[0072] Interaction Spectrum: Range of Interactions: The interaction spectrum supports a wide range of interactions from deterministic (fixed responses) to non-deterministic (variable responses). The spectrum enables progressive testing of the Al starting from controlled rule-based environments and advancing to complex real-life scenarios.

[0073] Virtual and Physical Interaction: The system allows for interactions with both virtual and physical digital twins, ensuring comprehensive testing across different media. The system includes interactions through digital interfaces and direct physical interactions with androids.

[0074] 2. Stages of Interaction

[0075] Stage 1: Deterministic Interactions:

[0076] Basic Functionality: Deterministic interactions involve rule-based, fixed sequences of questions and answers. Deterministic interactions ensure the basic functionality and logical consistency of the conversational Al.

[0077] Stage 2: Semi-Deterministic Interactions:

[0078] Testing Adaptability: The system allows for slight variability in responses. The semi- deterministic interactions affect the Al's ability to adapt to non-standard but predictable variations in interactions.

[0079] Stage 3: Guided Conversational Interactions:

[0080] Language Model Utilization: The guided conversational interactions stage utilizes language models within certain constraints to improve natural language understanding and response generation. The guided conversational interactions begin to introduce more complex interaction patterns.

[0081] Stage 4: Dynamic Conversational Interactions:

[0082] Advanced NLP Techniques: Employs advanced natural language processing (NLP) techniques to handle highly variable and dynamic interactions. The Al learns from each interaction to enhance its accuracy and relevance. Stage 5: Patient-Specific Customization:

[0083] Personalized Responses: At the patient-specific customization stage, the Al tailors its responses based on detailed patient profiles created during the digital twin setup. The patient-specific customization stage ensures the Al may effectively handle personalized interactions.

[0084] Stage 6: Realistic Scenario Simulation:

[0085] Complex Dialogues: Tests the Al in scenarios that closely mimic real-world situations, including complex, multi-turn dialogues. Realistic scenario simulation evaluates the Al's ability to maintain context over extended conversations.

[0086] Stage 7: Full-Scale Clinical Simulations:

[0087] High-Fidelity Simulations: Conducts extensive simulations using high-fidelity digital twins. The Al must demonstrate consistent, accurate, and empathetic interactions across a wide range of patient scenarios.

[0088] 3. Integration with Clinical Studies

[0089] Clinical Study Data Integration:

[0090] Seamless Integration: The simulation framework integrates seamlessly with clinical study protocols. Data generated from simulations may be used to refine study designs and validate the effectiveness of conversational Al before human trials.

[0091] Continuous Feedback Loop:

[0092] Iterative Improvement: A continuous feedback loop is established where simulation data is analyzed to iteratively improve the Al. The continuous feedback loop ensures that the Al evolves based on the latest findings and remains up-to-date with current medical knowledge.

[0093] 4. Regulatory Compliance

[0094] HIPAA, PIPED A, and GDPR Compliance: Data Privacy and Security: The framework incorporates stringent data privacy and security measures to comply with regulatory standards. Personal data is anonymized, and all data transmissions are encrypted. Access to data is strictly controlled.

[0095] Data Privacy Measures:

[0096] Protection Techniques: Utilizes data masking, pseudonymization, and role-based access control to protect patient information and ensure compliance with regulatory requirements.

[0097] 5. Al and Machine Learning Integration

[0098] Conversational Al Optimization:

[0099] Real-Time Feedback: Uses advanced machine learning algorithms to dynamically adjust and optimize conversational flows based on real-time feedback from interactions.

[0100] Reporting and Analytics Engine:

[0101] Detailed Analytics: The platform includes a robust reporting engine that generates detailed analytics on Al performance, interaction quality, and user satisfaction. The insights are crucial for clinical study publication and ongoing Al improvement.

[0102] 6. Versatility Across Industries

[0103] Healthcare Focus:

[0104] Primary Design: While primarily designed for healthcare, the framework's modular design allows the system to be adapted for other industries such as finance, education, and retail.

[0105] Configurable Modules:

[0106] Industry Adaptation: The platform's architecture supports configurable modules that may be tailored to specific industry requirements, ensuring versatility and broad applicability.

[0107] 7. Ethical Considerations Digital Twin Ethics:

[0108] Ethical Guidelines: Ensures ethical guidelines are followed in the creation and use of digital twins, including informed consent, data privacy, and avoiding the replication of real individuals without explicit permission.

[0109] Bias Mitigation:

[0110] Fairness and Inclusion: Continuous efforts are made to identify and mitigate biases in Al interactions. Continuous efforts include regular audits, diverse training data sets, and inclusive design practices.

[0111] The disclosed Automated Clinical Study Simulation System for Conversational Al in Healthcare and Other Industries represents a significant advancement in the development, testing, and validation of Al-driven diagnostic and therapeutic solutions. By leveraging advanced simulation techniques, including the use of digital twins and sophisticated natural language processing, the disclosed system addresses the inherent challenges of integrating conversational Al into clinical practice.

[0112] The disclosed system not only ensures the rigorous testing and validation of Al systems in a controlled, scalable, and repeatable manner but also facilitates compliance with stringent regulatory requirements. The ability to simulate a wide range of patient interactions and clinical scenarios without the need for human subjects accelerates the innovation cycle while maintaining the highest standards of patient safety and data integrity.

[0113] The disclosed system provides specific technological improvements including: a) enhanced natural-language generation reliability through protocol-compiled constraint engines, b) improved patient modeling fidelity via digital twin behavioral representations, c) comprehensive simulation coverage through staged deterministic-to-stochastic testing, and d) strengthened runtime data protection through automated de-identification and encryption protocols. The versatility of the framework extends its applicability beyond healthcare to other industries such as finance, customer service, and education, where robust Al validation is critical. The integrated reporting engine and continuous feedback loop further enhance the system's capability to evolve and improve, ensuring that the Al systems remain current and effective.

[0114] In conclusion, the disclosed system provides a comprehensive, ethical, and efficient approach to developing and validating conversational Al systems, paving the way for safe and effective deployment in various critical applications. By bridging the gap between simulation and real-world testing, the disclosed system accelerates the adoption of innovative Al solutions while ensuring compliance with regulatory standards and addressing the unique challenges of conversational Al in diverse industries.

[0115] The disclosed automated clinical study simulation system for conversational Al addresses several significant problems in the healthcare industry, particularly in the development and validation of Al-driven diagnostic and therapeutic tools.

[0116] Further, the automated clinical study simulation system provides a robust, scalable, and ethical solution for developing and validating conversational Al in healthcare and other industries. The ethical solution ensures that Al systems are safe, reliable, and compliant with regulatory standards before the systems are used in real-world applications.

[0117] While existing technologies and systems address various aspects of conversational Al in healthcare, the systems often fall short of providing a comprehensive solution that includes multi-stage interaction testing, extensive simulation capabilities, and built-in regulatory compliance. The disclosed system combines the elements, offering a robust framework for the thorough validation of conversational Al systems across different levels of interaction complexity and ensuring compliance with regulatory standards. The comprehensive approach addresses the critical gaps left by existing solutions and provides a scalable, efficient, and ethical pathway for developing safe and effective AL driven healthcare applications. The Automated Clinical Study Simulation System for Conversational Al in Healthcare and Other Industries solves several critical problems associated with the development, testing, and validation of Al-driven healthcare solutions.

[0118] Resource-Intensive Clinical Trials:

[0119] The system uses digital twins, which are virtual representations of patients with detailed medical histories and conditions, eliminating the need for large-scale human trials during initial testing phases, significantly reducing resource requirements.

[0120] Unlike existing solutions, the disclosed system may simulate numerous interactions quickly and cost-effectively, allowing for extensive testing without the logistical and financial burden of traditional clinical trials.

[0121] Traditional clinical trials are expensive, time-consuming, and require significant human and material resources. Conducting the trials involves recruiting patients, coordinating clinical sites, and managing extensive regulatory documentation, which may delay the deployment of new healthcare technologies.

[0122] For Patient Safety and Ethical Concerns:

[0123] By conducting simulations instead of real patient interactions during the development phase, the system ensures that any issues with the Al may be identified and resolved without risking patient safety.

[0124] The controlled simulation environment is safer and more ethical than using real patients for early-stage Al testing, addressing safety concerns more effectively than current methods.

[0125] Testing new Al systems on real patients may pose risks to patient safety, especially in the early stages of development when the Al's reliability and accuracy are not yet proven. Ethical concerns also arise regarding the use of patient data and ensuring informed consent. For Regulatory Compliance Challenges:

[0126] The system includes built-in compliance features for regulations like HIPAA, PIPEDA, and GDPR. Further, the system uses data anonymization, encryption, and strict access controls to protect patient data.

[0127] While other systems may focus on functionality, the disclosed system ensures that all regulatory requirements are met from the ground up, simplifying the path to regulatory approval.

[0128] The regulatory landscape for Al in healthcare is complex and constantly evolving. Ensuring compliance with stringent data privacy and security regulations such as HIPAA, PIPEDA, and GDPR is a major challenge for developers of Al systems. The complexity may hinder innovation and delay the approval of new technologies.

[0129] For variability in patient interactions:

[0130] The multi-stage interaction framework ranges from deterministic to non-deterministic models, simulating a wide range of patient interactions and scenarios.

[0131] The structured approach allows for progressive testing, ensuring that the Al may handle both simple and complex interactions effectively, unlike existing systems that may not fully address the variability.

[0132] Conversational Al systems must handle a wide range of patient interactions, which may vary significantly in terms of language, symptoms, and medical histories. Traditional testing methods may not sufficiently capture the variability, leading to Al systems that perform well in controlled environments but fail in real-world scenarios.

[0133] For continuous learning and adaptation:

[0134] The system employs a continuous feedback loop where simulation data is used to iteratively improve the Al models. The system ensures the Al stays current with the latest medical knowledge and practices. The ongoing improvement process is more advanced than what is typically seen in current solutions, which may not support continuous adaptation based on real-time feedback. Al systems, particularly those based on machine learning, require continuous updates and improvements based on new data and interactions. Traditional clinical trials do not typically accommodate, need for iterative learning and real-time feedback, making it difficult to keep Al systems up-to-date with the latest medical knowledge and practices.

[0135] The use of digital twins and advanced simulation techniques allows for scalable and repeatable testing across a wide range of scenarios. Unlike other solutions that might be limited in scope and scalability, the system may conduct extensive, repeatable clinical trials, ensuring thorough validation before real- world deployment.

[0136] Current methods for testing conversational Al are often not scalable or repeatable, limiting the ability to conduct extensive and thorough validation, resulting in Al systems that are not fully vetted, potentially leading to issues in real-world deployment.

[0137] Comprehensive Framework: The disclosed system integrates a multi-stage simulation framework, regulatory compliance, continuous feedback, and scalability in one cohesive system. Existing conventional solutions often focus on individual aspects rather than providing an end-to-end solution.

[0138] Ethical and Safe Testing: By using digital twins and simulations, the system avoids ethical concerns and safety risks associated with testing on real patients, a significant improvement over current practices.

[0139] Regulatory Integration: Built-in compliance features ensure that the Al systems are designed with regulatory requirements in mind, reducing the time and complexity of achieving regulatory approval compared to other systems.

[0140] There are several existing systems that attempt to solve the problems associated with developing and validating conversational Al in healthcare. The systems vary in their approaches and functionalities but share the common goal of enhancing the reliability, safety, and efficacy of Al-driven healthcare solutions.

[0141] 1. Nuance and Microsoft's DAX System: Functionality: The system focuses on automating clinical documentation using conversational Al. The system integrates into clinical workflows to streamline the documentation process.

[0142] Limitations: While the system improves efficiency and reduces clinician workload, the system does not provide a comprehensive simulation framework for rigorous testing and validation of Al interactions with patients before deployment. 's Involve XR Platform:

[0143] Functionality: The platform uses large language models to create customizable Al-driven conversational simulations in virtual reality (VR). It aims to replicate realistic patient interactions for training purposes.

[0144] Limitations: Although the platform offers an immersive experience and addresses training needs, the platform lacks a structured multi-stage interaction framework and does not focus extensively on regulatory compliance or scalable testing methods. d Scientific's HAL® S5301:

[0145] Functionality: The advanced patient simulator provides high-fidelity simulations for multidisciplinary medical training, including life-like patient interactions and responses.

[0146] Limitations: While the simulator excels in creating realistic simulations for training, the system does not specifically address the iterative testing and validation of conversational Al systems across a spectrum of interaction complexities. verse:

[0147] Functionality: The platform uses generative Al to simulate a variety of patient and healthcare provider interactions, focusing on improving communication skills through realistic, adaptive scenarios.

[0148] Limitations: SimConverse is primarily aimed at training communication skills rather than providing a comprehensive, multi-stage testing framework for conversational Al systems. The system also does not emphasize regulatory compliance measures. 5. General Conversational Al Systems:

[0149] Functionality: Many conversational Al systems are designed for specific tasks such as symptom checking, appointment scheduling, and patient education. The systems often integrate with electronic health records (EHR) and use machine learning to improve over time.

[0150] Limitations: The systems typically lack the ability to simulate full clinical studies and do not provide a controlled, scalable environment for extensive testing before real-world deployment. Additionally, the systems often fall short in terms of compliance with stringent healthcare regulations and continuous iterative improvement based on simulation feedback.

[0151] While existing technologies and systems address various aspects of conversational Al in healthcare, the systems often fall short of providing a comprehensive solution that includes multi-stage interaction testing, extensive simulation capabilities, and built-in regulatory compliance. The proposed system uniquely combines the elements, offering a robust framework for the thorough validation of conversational Al systems across different levels of interaction complexity and ensuring compliance with regulatory standards. The comprehensive approach addresses the critical gaps left by existing solutions and provides a scalable, efficient, and ethical pathway for developing safe and effective Al-driven healthcare applications.

[0152] The Automated Clinical Study Simulation System for Conversational Al in Healthcare and Other Industries solves several critical problems associated with the development, testing, and validation of Al-driven healthcare solutions. Here’s a detailed explanation of how the system addresses the issues and outperforms existing solutions:

[0153] Solving Key Problems:

[0154] 1. Resource-Intensive Clinical Trials:

[0155] Solution: The system uses digital twins, which are virtual representations of patients with detailed medical histories and conditions. The system eliminates the need for large-scale human trials during initial testing phases, significantly reducing resource requirements.

[0156] Advantage: Unlike existing solutions, the system may simulate numerous interactions quickly and cost-effectively, allowing for extensive testing without the logistical and financial burden of traditional clinical trials. Safety and Ethical Concerns:

[0157] Solution: By conducting simulations instead of real patient interactions during the development phase, the system ensures that any issues with the Al may be identified and resolved without risking patient safety.

[0158] Advantage: The controlled simulation environment is safer and more ethical than using real patients for early-stage Al testing, addressing safety concerns more effectively than current methods. ory Compliance Challenges:

[0159] Solution: The system includes built-in compliance features for regulations like HIPAA, PIPEDA, and GDPR. The system uses data anonymization, encryption, and strict access controls to protect patient data.

[0160] Advantage: While other systems may focus on functionality, the system ensures that all regulatory requirements are met from the ground up, simplifying the path to regulatory approval. lity in Patient Interactions:

[0161] Solution: The multi-stage interaction framework ranges from deterministic to non-deterministic models, simulating a wide range of patient interactions and scenarios.

[0162] Advantage: The structured approach allows for progressive testing, ensuring that the Al may handle both simple and complex interactions effectively, unlike existing systems that may not fully address the variability. 5. Continuous Learning and Adaptation:

[0163] Solution: The system employs a continuous feedback loop where simulation data is used to iteratively improve the Al models. The system ensures the Al stays current with the latest medical knowledge and practices.

[0164] Advantage: The ongoing improvement process is more advanced than what is typically seen in current solutions, which may not support continuous adaptation based on real-time feedback.

[0165] 6. Lack of Scalable and Repeatable Testing Methods:

[0166] Solution: The use of digital twins and advanced simulation techniques allows for scalable and repeatable testing across a wide range of scenarios. Advantage: Unlike other solutions that might be limited in scope and scalability, the system may conduct extensive, repeatable clinical trials, ensuring thorough validation before real-world deployment.

[0167] Working Better Than Existing Solutions:

[0168] Comprehensive Framework: The system integrates a multi-stage simulation framework, regulatory compliance, continuous feedback, and scalability in one cohesive system. Existing solutions often focus on individual aspects rather than providing an end-to-end solution.

[0169] Ethical and Safe Testing: By using digital twins and simulations, the system avoids ethical concerns and safety risks associated with testing on real patients, a significant improvement over current practices.

[0170] Regulatory Integration: Built-in compliance features ensure that the Al systems are designed with regulatory requirements in mind, reducing the time and complexity of achieving regulatory approval compared to other systems. Scalability: The ability to simulate millions of interactions efficiently sets the system apart from traditional methods and other Al solutions that lack the level of scalability.

[0171] The system addresses critical gaps left by existing technologies, providing a robust, scalable, and ethical solution for the development and validation of conversational Al in healthcare and other industries. By ensuring thorough testing, regulatory compliance, and continuous improvement, the system offers a superior approach to integrating Al into clinical practice and other critical applications.

[0172] The ability to simulate millions of interactions efficiently sets the system apart from traditional methods and other Al solutions that lack the level of scalability.

[0173] The disclosed system addresses critical gaps left by existing technologies, providing a robust, scalable, and ethical solution for the development and validation of conversational Al in healthcare and other industries. By ensuring thorough testing, regulatory compliance, and continuous improvement, the disclosed offers a superior approach to integrating Al into clinical practice and other critical applications.

[0174] Further, individual components (elements) that make up the best version of the Automated Clinical Study Simulation System for Conversational Al in Healthcare and Other Industries may include:

[0175] 1. Simulation Framework:

[0176] Digital Twins

[0177] Interaction Spectrum Module

[0178] 2. Stages of Interaction:

[0179] Deterministic Interactions Module

[0180] Semi-Deterministic Interactions Module

[0181] Guided Conversational Interactions Module Dynamic Conversational Interactions Module Patient-Specific Customization Module Realistic Scenario Simulation Module

[0182] Full-Scale Clinical Simulations Module ration with Clinical Studies:

[0183] Clinical Study Data Integration Interface Continuous Feedback Loop System latory Compliance:

[0184] HIPAA Compliance Module

[0185] PIPEDA Compliance Module

[0186] GDPR Compliance Module

[0187] Data Privacy Measures

[0188] Data Encryption Mechanism

[0189] Access Control System d Machine Learning Integration:

[0190] Conversational Al Optimization Engine

[0191] Machine Learning Algorithms

[0192] Real-Time Feedback System

[0193] Reporting and Analytics Engine tility Across Industries:

[0194] Configurable Modules for Industry Adaptation API Integration Capabilities al Considerations:

[0195] Digital Twin Ethics Guidelines

[0196] Bias Mitigation Techniques Interface Components:

[0197] User Interface for Healthcare Providers User Interface for Patients

[0198] Mobile and PC Cloud Applications

[0199] 9. Support and Training Modules:

[0200] Training Materials for Healthcare Professionals

[0201] Ongoing Support and Maintenance System

[0202] The components together form a comprehensive system that addresses the critical needs of developing, validating, and deploying conversational Al in healthcare and other industries.

[0203] The components of the Automated Clinical Study Simulation System for Conversational Al interact seamlessly to create a comprehensive and efficient platform for developing, testing, and validating Al-driven healthcare solutions. Here’s a detailed description of how the components interact:

[0204] 1. Simulation Framework:

[0205] Digital Twins: Serve as the foundation by creating virtual representations of patients. The digital twins interact with the conversational Al, providing realistic scenarios based on diverse medical histories and conditions.

[0206] Interaction Spectrum Module: Manages the progression of interactions from deterministic to non-deterministic. The system ensures that the digital twins may engage in various levels of conversation complexity with the Al.

[0207] 2. Stages of Interaction:

[0208] Deterministic Interactions Module: Initiates the testing process with rule-based, fixed question-and-answer sequences. The module ensures the basic functionality of the conversational Al.

[0209] Semi-Deterministic Interactions Module: Introduces slight variability, allowing the Al to adapt to minor deviations while maintaining a structured path. Guided Conversational Interactions Module: Utilizes more sophisticated language models to interpret and respond within certain constraints, enhancing natural language understanding.

[0210] Dynamic Conversational Interactions Module: Handles highly variable interactions using advanced natural language processing (NLP) techniques, learning from each interaction.

[0211] Patient-Specific Customization Module: Tailors Al responses based on detailed patient profiles created during the digital twin setup, ensuring personalized interactions.

[0212] Realistic Scenario Simulation Module: Simulates real-world situations with complex, multi-turn dialogues to test the Al’s ability to maintain context over extended conversations.

[0213] Full-Scale Clinical Simulations Module: Conducts extensive simulations with high- fidelity digital twins to demonstrate consistent, accurate, and empathetic interactions.

[0214] 3. Integration with Clinical Studies:

[0215] Clinical Study Data Integration Interface: Collects and integrates simulation data with clinical study protocols, refining study designs and validating effectiveness before human trials.

[0216] Continuous Feedback Loop System: Analyzes simulation data and iteratively improves the Al, ensuring the system evolves based on the latest findings and medical knowledge.

[0217] 4. Regulatory Compliance:

[0218] HIPAA, PIPED A, and GDPR Compliance Modules: Ensure data privacy and security, anonymizing personal data and encrypting transmissions to meet regulatory standards.

[0219] Data Privacy Measures: Implement techniques like data masking and pseudonymization to protect patient information. Data Encryption Mechanism: Secures all data transmissions and storage.

[0220] Access Control System: Manages user access to sensitive information based on roles and permissions.

[0221] 5. Al and Machine Learning Integration:

[0222] Conversational Al Optimization Engine: Uses machine learning algorithms to refine the Al’s conversational capabilities based on real-time feedback from interactions.

[0223] Reporting and Analytics Engine: Generates detailed analytics on Al performance, interaction quality, and user satisfaction to support clinical study publication and ongoing Al improvement.

[0224] 6. Versatility Across Industries:

[0225] Configurable Modules for Industry Adaptation: Allow the core capabilities of the framework to be tailored to specific industry needs, ensuring the system’s adaptability beyond healthcare.

[0226] API Integration Capabilities: Enable seamless data exchange with third-party systems, such as EHR and HIS, using standards like HL7 and FHIR.

[0227] 7. Ethical Considerations:

[0228] Digital Twin Ethics Guidelines: Ensure informed consent, data privacy, and avoid the replication of real individuals without permission.

[0229] Bias Mitigation Techniques: Conduct regular audits and use diverse training data sets to ensure fairness in Al interactions.

[0230] 8. User Interface Components:

[0231] User Interface for Healthcare Providers: Allows clinicians to interact with the system, review simulation data, and adjust settings.

[0232] User Interface for Patients: Provides a platform for patient interaction with the Al in a controlled environment. Mobile and PC Cloud Applications: Ensure accessibility from various devices and locations, enhancing usability for both learners and practitioners.

[0233] 9. Support and Training Modules:

[0234] Training Materials for Healthcare Professionals: Provide resources to help users understand and effectively utilize the system.

[0235] Ongoing Support and Maintenance System: Offers continuous assistance and updates to ensure the system remains current and functional.

[0236] Interaction Flow associated with the disclosed system may include:

[0237] 1. Initialization:

[0238] The simulation framework sets up digital twins with detailed profiles.

[0239] Interaction spectrum modules determine the starting point of interactions based on the test phase.

[0240] 2. Simulation Execution:

[0241] Digital twins engage in interactions with the Al, progressing through various stages from deterministic to non-deterministic.

[0242] Each interaction stage tests specific aspects of the Al’s performance, feeding data back into the continuous feedback loop system.

[0243] 3. Data Integration and Analysis:

[0244] Simulation data is integrated into clinical study protocols via the data integration interface.

[0245] The continuous feedback loop analyzes the data, updating Al models for improved performance.

[0246] 4. Compliance and Security: Data privacy measures, encryption mechanisms, and access controls ensure regulatory compliance throughout the process.

[0247] 5. Reporting and Optimization:

[0248] The reporting engine generates detailed analytics and insights, supporting further Al optimization and clinical study publication.

[0249] 6. Adaptation and Versatility:

[0250] Configurable modules and API capabilities enable adaptation for various industries and seamless integration with existing systems.

[0251] 7. Ethical Oversight:

[0252] Ethical guidelines and bias mitigation techniques ensure the system operates fairly and responsibly.

[0253] By integrating the components seamlessly, the automated clinical study simulation system ensures a comprehensive, scalable, and ethical approach to developing and validating conversational Al, outperforming existing solutions in robustness and adaptability.

[0254] Further, the components of the Automated Clinical Study Simulation System for Conversational Al work both individually and together. Further, each component's role and the interactions in achieving the desired system performance may be as follows:

[0255] Individual Components

[0256] 1. Simulation Framework

[0257] Digital Twins: Create virtual representations of patients, encompassing diverse medical histories, symptoms, and behaviors. The framework serves as the primary subject for Al interactions, enabling extensive and varied testing without involving real patients. Interaction Spectrum Module: Manages the types of interactions, from deterministic (fixed responses) to non-deterministic (Al-driven variable responses), providing a gradient of testing environments to assess Al robustness.

[0258] 2. Stages of Interaction

[0259] Deterministic Interactions Module: Ensures the Al may handle straightforward, rulebased interactions, verifying fundamental capabilities.

[0260] Semi-Deterministic Interactions Module: Introduces slight variations to test Al's adaptability to non-standard but predictable responses.

[0261] Guided Conversational Interactions Module: Uses more complex language models to test the Al's natural language processing capabilities within controlled scenarios.

[0262] Dynamic Conversational Interactions Module: Evaluates Al's performance in highly variable and unpredictable interactions, essential for real-world applications.

[0263] Patient-Specific Customization Module: Tailors interactions based on detailed patient profiles, ensuring the Al may handle personalized healthcare scenarios.

[0264] Realistic Scenario Simulation Module: Engages Al in complex, real-world-like scenarios to assess its ability to maintain context and relevance over extended dialogues.

[0265] Full-Scale Clinical Simulations Module: Conducts extensive simulations to validate Al consistency and reliability across numerous interactions.

[0266] 3. Integration with Clinical Studies

[0267] Clinical Study Data Integration Interface: Facilitates the seamless integration of simulation data into clinical study protocols, ensuring the data's applicability to real- world validation.

[0268] Continuous Feedback Loop System: Continuously refines Al based on simulation data, ensuring ongoing improvements and adaptation to new findings.

[0269] 4. Regulatory Compliance HIPAA, PIPED A, and GDPR Compliance Modules: Ensure data handling meets regulatory standards, protecting patient information, and ensuring legal compliance.

[0270] Data Privacy Measures: Techniques like data masking and pseudonymization safeguard patient identity.

[0271] Data Encryption Mechanism: Secures data transmission and storage.

[0272] Access Control System: Restricts data access based on user roles, ensuring only authorized personnel may view sensitive information.

[0273] 5. Al and Machine Learning Integration

[0274] Conversational Al Optimization Engine: Utilizes machine learning to enhance Al performance based on interaction data.

[0275] Reporting and Analytics Engine: Provides detailed insights into Al performance and interaction quality, supporting continuous improvement and validation efforts.

[0276] 6. Versatility Across Industries

[0277] Configurable Modules for Industry Adaptation: Allow customization for different industry needs, extending the system's applicability beyond healthcare.

[0278] API Integration Capabilities: Enable seamless interaction with third-party systems, such as EHR and HIS, using standardized data protocols like HL7 and FHIR.

[0279] 7. Ethical Considerations

[0280] Digital Twin Ethics Guidelines: Ensure ethical creation and use of digital twins, respecting privacy and consent.

[0281] Bias Mitigation Techniques: Regular audits and diverse training datasets ensure fair and unbiased Al interactions.

[0282] 8. User Interface Components User Interface for Healthcare Providers: Provides tools for clinicians to interact with the system, review data, and manage simulations.

[0283] User Interface for Patients: Allows simulated patient interactions in a controlled environment.

[0284] Mobile and PC Cloud Applications: Ensure accessibility and usability across various devices and locations.

[0285] 9. Support and Training Modules

[0286] Training Materials for Healthcare Professionals: Offer resources for effective system use.

[0287] Ongoing Support and Maintenance System: Provide continuous assistance and updates to ensure system functionality and relevance.

[0288] Combined Functionality

[0289] 1. Initialization and Setup

[0290] Digital twins are created with detailed profiles and are ready to interact with the Al through the interaction spectrum module. The system's compliance modules ensure data is securely handled from the start.

[0291] 2. Simulation Execution

[0292] The Al engages in interactions starting from deterministic modules, progressively moving to more complex interaction stages. Each stage tests different aspects of Al performance, from basic functionality to handling complex, real-world scenarios.

[0293] The Al's responses and behavior are continuously monitored and refined through the continuous feedback loop system, ensuring ongoing improvements.

[0294] 3. Data Integration and Analysis Simulation data is collected and integrated into clinical study protocols via the clinical study data integration interface, providing a robust foundation for real-world validation.

[0295] The reporting and analytics engine generates comprehensive reports on Al performance, which are used for further refinement and validation.

[0296] 4. Regulatory Compliance and Security

[0297] Throughout the process, the compliance modules ensure that all data handling adheres to regulatory standards, protecting patient information and ensuring legal compliance.

[0298] Data privacy measures, encryption mechanisms, and access controls maintain the security and integrity of sensitive information.

[0299] 5. Optimization and Versatility

[0300] The Al optimization engine uses the collected data to fine-tune Al models, enhancing performance across various scenarios.

[0301] Configurable modules and API capabilities ensure that the system may be adapted for use in different industries, extending its applicability beyond healthcare.

[0302] 6. Ethical Oversight

[0303] Ethical guidelines and bias mitigation techniques are applied throughout the process, ensuring fair and responsible Al interactions.

[0304] User interfaces provide accessible tools for both healthcare providers and patients, facilitating effective use and interaction with the system.

[0305] By integrating the components seamlessly, the Automated Clinical Study Simulation System provides a comprehensive, scalable, and ethical approach to developing, testing, and validating conversational Al. Each component plays a crucial role individually, and interactions create a robust framework that ensures the Al is thoroughly vetted and ready for real-world application, outperforming existing solutions in terms of functionality, compliance, and adaptability. Further, creating the automated clinical study simulation system for conversational Al involves several detailed steps, ranging from conceptual design and development to testing and deployment.

[0306] 1. Conceptual Design and Planning a. Define Objectives

[0307] Identify Goals: Clearly outline the goals of the system, such as enhancing AI- driven diagnostic tools, ensuring regulatory compliance, and enabling scalable clinical simulations.

[0308] Specify Requirements: Determine the necessary components, including digital twins, interaction spectrum modules, compliance features, and integration capabilities. b. Research and Development

[0309] Literature Review: Conduct a thorough review of existing technologies and frameworks in Al, clinical simulations, and regulatory standards.

[0310] Feasibility Study: Assess the technical and economic feasibility of the project.

[0311] 2. System Architecture Design a. Simulation Framework Design

[0312] Digital Twins Creation: Develop algorithms for creating virtual representations of patients, including comprehensive medical histories, symptoms, and behavioral patterns.

[0313] Interaction Spectrum Module: Design the module to manage various interaction stages from deterministic to non-deterministic. b. Stages of Interaction Development

[0314] Stage Design: Create detailed designs for each interaction stage, from deterministic to full-scale clinical simulations, involving defining the complexity and variability of interactions at each stage. 3. Development and Integration a. Development of Individual Components

[0315] Digital Twins: Implement the algorithms to generate digital twins with realistic patient data.

[0316] Interaction Modules: Develop the interaction spectrum module to handle different levels of interaction complexity. b. Al and Machine Learning Integration

[0317] Conversational Al Engine: Develop and integrate the Al engine capable of natural language processing and dynamic response generation.

[0318] Machine Learning Algorithms: Implement machine learning algorithms to optimize Al performance based on interaction data. c. Compliance and Security Features

[0319] Compliance Modules: Develop modules to ensure HIPAA, PIPED A, and GDPR compliance. Implement data encryption, anonymization, and access control mechanisms.

[0320] Data Privacy Measures: Implement data masking, pseudonymization, and secure data storage solutions. d. User Interfaces

[0321] Healthcare Provider Interface: Design and develop an interface for healthcare providers to interact with the system and manage simulations.

[0322] Patient Interface: Create an interface for patient interactions within the simulation environment.

[0323] 4. Testing and Validation a. Component Testing

[0324] Unit Testing: Conduct unit testing for each component to ensure the function works as intended. Integration Testing: Test the integration of components to ensure seamless interaction and data flow. b. Simulation Testing

[0325] Scenario Testing: Test the Al in various simulated scenarios across different interaction stages to evaluate performance, accuracy, and reliability.

[0326] Compliance Testing: Ensure all data handling processes meet regulatory standards.

[0327] 5. Deployment and Training a. System Deployment

[0328] Cloud Deployment: Deploy the system on cloud platforms to ensure scalability and accessibility.

[0329] API Integration: Integrate with third-party systems such as EHR and HIS using standard APIs. b. Training and Support

[0330] User Training: Develop training programs for healthcare providers and patients on how to use the system.

[0331] Ongoing Support: Establish a support system for continuous assistance and updates.

[0332] 6. Continuous Improvement a. Feedback Loop Implementation

[0333] Data Collection: Continuously collect data from simulations to identify areas for improvement.

[0334] Al Optimization: Use the feedback loop to iteratively improve Al performance based on real-time data and evolving medical knowledge. b. Regular Audits and Updates Compliance Audits: Regularly audit the system for compliance with regulatory standards.

[0335] System Updates: Implement updates and improvements based on new regulations, user feedback, and technological advancements.

[0336] By following the detailed steps, the disclosed system may be a robust, scalable, and compliant Automated Clinical Study Simulation System for Conversational Al. Each component plays a critical role individually, and interactions ensure the system functions effectively to meet its goals. The approach not only enhances the development and validation of Al-driven healthcare solutions but also ensures the system is safe, reliable, and ready for real-world deployment.

[0337] The following are the steps associated with the system:

[0338] 1. Getting Started

[0339] System Requirements

[0340] Hardware: Compatible with standard medical simulation hardware setups.

[0341] Software: Requires the latest versions of supported operating systems (Windows, macOS, Linux).

[0342] Internet: Stable internet connection for cloud-based operations.

[0343] Installation and Setup

[0344] Download and Install: Obtain the installation package from the official website.

[0345] Follow the on-screen instructions to install the software.

[0346] Create an Account: Register an account using professional credentials.

[0347] Initial Configuration: Set up initial configurations, including compliance settings and data encryption preferences.

[0348] 2. User Interface Overview

[0349] Healthcare Provider Interface

[0350] Dashboard: Access to all main features, including simulation management, patient profiles, and data analysis.

[0351] Simulation Control: Tools for starting, pausing, and stopping simulations.

[0352] Data Access: View and manage simulation data and reports.

[0353] Patient Interface Interactive Dialogues: Engage with the Al through a user-friendly interface designed for realistic patient interactions.

[0354] Scenario Settings: Customize scenarios to match specific clinical conditions and interaction complexities. Creating and Managing Digital Twins

[0355] Generating Digital Twins

[0356] Create New Twin: Use the digital twin generator tool to create a new virtual patient.

[0357] Input Data: Enter detailed medical histories, symptoms, and behavioral patterns.

[0358] Save Profile: Save the digital twin profile for future simulations.

[0359] Customizing Patient Profiles

[0360] Edit Profiles: Modify existing digital twin profiles to match new clinical scenarios.

[0361] Update Histories: Add or update medical histories and symptoms as needed.

[0362] Behavioral Adjustments: Customize behavioral patterns to simulate different patient responses. Conducting Simulations

[0363] Interaction Stages

[0364] Deterministic Interactions: Start with basic, rule-based interactions to test fundamental Al functionalities.

[0365] Semi-Deterministic and Guided Conversations: Progress to more complex interactions, allowing for slight variability and guided responses.

[0366] Dynamic and Realistic Scenarios: Engage in highly variable, real-life scenarios to test the Al’s adaptability and context maintenance.

[0367] Full-Scale Clinical Simulations: Conduct extensive simulations to ensure the Al’s reliability across numerous interactions.

[0368] Running Simulations

[0369] Select Scenario: Choose a predefined or customized scenario for the simulation. Start Simulation: Initiate the simulation from the dashboard.

[0370] Monitor Progress: Use real-time monitoring tools to oversee the simulation and gather data. For example, in some embodiments, at a deterministic stage, the simulation may involve a chatbot guiding a patient through a structured mental health questionnaire with predefined branching logic. At a later, non-deterministic stage, the system may simulate an open-ended therapy conversation with a virtual patient persona, where a generative Al model dynamically responds to patient inputs while maintaining therapeutic context. Data Integration and Analysis

[0371] Integrating with Clinical Study Protocols

[0372] Data Export: Export simulation data to integrate with clinical study protocols. Analysis Tools: Use built-in tools to analyze data and refine study designs. Validation: Validate the effectiveness of conversational Al based on simulation outcomes. Regulatory Compliance and Security

[0373] Ensuring Data Privacy

[0374] Anonymization: Use data anonymization techniques to protect patient identities. Encryption: Enable data encryption to secure information during transmission and storage.

[0375] Managing Access Controls

[0376] Role-Based Access: Set up role-based access controls to ensure only authorized personnel may access sensitive data.

[0377] Audit Logs: Maintain audit logs to track data access and modifications. Optimization and Reporting Continuous Feedback Loop

[0378] Data Collection: Automatically collect data from each simulation. Al Optimization: Use the feedback loop to iteratively improve Al performance. Generating Reports

[0379] Detailed Analytics: Generate detailed reports on Al performance, interaction quality, and user satisfaction.

[0380] Custom Reports: Customize reports based on specific requirements for clinical study publication and ongoing improvement. Support and Maintenance User Training

[0381] Training Programs: Access training materials and programs to understand the system’s functionalities.

[0382] Webinars and Workshops: Participate in regular webinars and workshops for continuous learning.

[0383] Getting Support

[0384] Help Desk: Contact the help desk for any technical assistance or troubleshooting. Online Resources: Use online resources, including FAQs and user manuals, for self-help.

[0385] By following the steps, users may effectively utilize the Automated Clinical Study Simulation System for Conversational Al, ensuring thorough testing, validation, and optimization of Al-driven healthcare solutions.

[0386] Following are the key aspects of the disclosed system and method:

[0387] 1. A system for automating clinical studies using conversational Al comprising: a simulation framework configured to create digital twins of patients with varying characteristics and conditions, wherein the digital twins include both virtual models and physical embodiments such as androids; an interaction spectrum module that supports a range of interactions from deterministic to non-deterministic; a plurality of stages for testing the conversational Al, each stage representing increasing complexity and realism of interactions; integration capabilities with clinical study protocols to utilize simulation data for study refinement and validation; a continuous feedback loop to iteratively improve the Al based on simulation data; data privacy and security measures to comply with regulatory standards such as HIPAA, PIPEDA, and GDPR; an Al and machine learning module for optimizing conversational capabilities and generating detailed analytics; a reporting engine to produce comprehensive reports on Al performance and interaction quality. Further, the digital twins are configured to exhibit detailed virtual representations of patients, including specific medical histories, symptoms, and behavioral patterns, and physical embodiments such as androids capable of mimicking patient behaviors and responses. Further, the interaction spectrum module supports: deterministic interactions with fixed question and answer sequences; semi-deterministic interactions allowing for variability in responses; guided conversational interactions using language models with constraints; dynamic conversational interactions utilizing advanced natural language processing; patient- specific customization of responses; realistic scenario simulation with complex multi-turn dialogues; full-scale clinical simulations with high-fidelity digital twins, including both virtual and physical embodiments. Further, the system may integrate with electronic health records (EHR) and health information systems (HIS) for seamless data exchange; support for data standards such as HL7 and FHIR; integrating API capabilities to facilitate integration with third-party systems. Further, the continuous feedback loop is configured to analyze simulation data and update the Al to ensure the Al evolves based on the latest findings and remains current with medical knowledge. Further, the system includes data privacy measures such as data masking, pseudonymization, and role-based access control; encrypted data transmissions and strict access controls to protect patient information. Further, the Al and machine learning module utilizes real-time feedback from interactions to adjust response patterns and improve performance. Further, the reporting engine generates detailed analytics on Al performance, interaction quality, and user satisfaction for clinical study publication and ongoing Al improvement. Further, the simulation framework and digital twins are adaptable for use in industries other than healthcare, such as finance, education, and retail. Further, ethical guidelines are followed in the creation and use of digital twins, including informed consent, data privacy, and avoidance of real individual replication without explicit permission. Further, the system includes bias mitigation techniques, including regular audits, diverse training data sets, and inclusive design practices to ensure fairness in Al interactions. A method for automating clinical studies using the system, comprising: creating digital twins with varying characteristics and conditions; progressing through a plurality of interaction stages from deterministic to non- deterministic; integrating simulation data with clinical study protocols; utilizing a continuous feedback loop to iteratively improve the Al; ensuring data privacy and security compliance with regulatory standards; optimizing conversational Al using machine learning and generating detailed analytics; producing comprehensive reports for clinical study publication and ongoing improvement. Further, the interaction stages include: deterministic interactions; semi-deterministic interactions; guided conversational interactions; dynamic conversational interactions; patient- specific customization; realistic scenario simulation; full-scale clinical simulations. Further, the method includes integrating the system with EHR and HIS for seamless data exchange; supporting data standards such as HL7 and FHIR; and utilizing API capabilities for third-party system integration. Further, the continuous feedback loop analyzes simulation data to update the Al based on the latest medical knowledge. 16. Further, the method includes implementing data privacy measures to ensure regulatory compliance and protect patient information.

[0388] 17. Further, the real-time feedback is used to adjust Al response patterns and improve overall performance.

[0389] 18. Further, the method includes generating detailed analytics on Al performance and interaction quality for publication and improvement purposes.

[0390] 19. Further, the method is adaptable for use in other industries such as finance, education, and retail.

[0391] 20. Further, the method follows ethical guidelines in the creation and use of digital twins, including informed consent and data privacy considerations.

[0392] In some embodiments, a protocol-compiled constraint engine may be employed to transform a clinical protocol data into a finite-state control and token-level rule set that may be enforced during a conversational simulation to avoid off -protocol content drift; the technical problem of unreliable adherence of a language model to prescriptive clinical workflow may be addressed by compiling each protocol step into a state, associating a guard condition and an allowed intent schema with each state, and instrumenting a constrained-decoding wrapper that may prune disallowed tokens and intents at generation time; the engine may be implemented with a schema validator that may check each candidate response against an allowed slot set and a state transition table, or with a control-code approach that may inject a control embedding per protocol state; the specific technology improved may be natural-language generation reliability and protocol compliance.

[0393] In some embodiments, a patient digital twin representation may include a compositional model that may fuse a knowledge graph node for a patient entity with a parameter vector that may encode demographics, condition, and medication regimen to the granularity needed for a study; the technical problem of brittle hand-crafted persona scripts may be addressed by enabling a twin parameter to drive response style and content deterministically under a deterministic mode and stochastically under a non-deterministic mode; the representation may be implemented with a typed feature map that may be serialized as JSON Schema, or as a learned embedding that may be initialized from the feature map and may be updated by an alignment routine; the specific technology improved may be patient modeling fidelity for conversational simulation.

[0394] In some embodiments, a mode scheduler may be employed to progress an interaction from a deterministic mode to a non-deterministic mode according to a stage criterion so that capability may be benchmarked under increasing dialog complexity; the technical problem of evaluation blind spots in a single sampling regime may be addressed by calculating a stage criterion from turn count, error rate, or protocol milestone, selecting a deterministic beam search at early turns, and switching to a stochastic nucleus sampling beyond a threshold; the scheduler may be implemented as a simple threshold function or as a policy that may be learned from transcript difficulty; the specific technology improved may be simulation coverage and robustness testing.

[0395] In some embodiments, a deviation detector may analyze a simulation transcript data to identify a protocol deviation and may update a behavior model of the patient digital twin to reduce recurrence; the technical problem of silent non-compliance and slow remediation may be addressed by computing an alignment signal that may include a constraint violation type, a missing slot, or an unsupported claim, and by applying a lightweight parameter update or a rule insertion that may block the failure pattern in a subsequent run; the update may be implemented via low-rank adaptation to a response head or by a rule precedence change in an interpreter; the specific technology improved may be online model adaptation and continuous compliance.

[0396] In some embodiments, an analytics generator may compute a performance metric from a transcript and may tag a study report data with that metric to enable auditable benchmarking; the technical problem of opaque model quality may be addressed by parsing a transcript into atomic dialog acts, computing per-act precision for instruction adherence, and persisting a metric with a provenance pointer for post-hoc review; the implementation may include a deterministic parser or a learned act classifier with confidence calibration; the specific technology improved may be automated reporting and explainable evaluation. In some embodiments, a privacy layer may detect an identifier in a transcript, may replace the identifier with a token, and may encrypt a de-identified transcript at rest and in transit; the technical problem of leakage of personal data during simulation and reporting may be addressed by named-entity recognition tuned for clinical entities, reversible pseudonymization under a key hierarchy, and authenticated encryption using an envelope key; the implementation may employ a deterministic tokenizer for consistent re-identification under authorization or a format-preserving encryption mode for structured fields; the specific technology improved may be data security for clinical conversational content.

[0397] In some embodiments, a format engine may extract a format rule from a protocol and may transform and validate a study report data against the rule to guarantee machine- checkable conformance; the technical problem of manual document assembly and formatting error may be addressed by binding each report field to a protocol-defined schema element and by running a structural and semantic validation pass that may include unit validation, range validation, and cross-field dependency validation; the implementation may be a JSON-to-document template with a schema validator or a DSL- driven Tenderer that may emit a regulatory-ready artifact; the specific technology improved may be document generation and compliance automation.

[0398] In some embodiments, a response constraint derivation may compute a constraint from a protocol and may generate a constrained response that may satisfy a safety profile such as disallowing prescriptive advice or speculative diagnosis; the technical problem of unsafe generation may be addressed by formulating allowed intents and red-flag intents and by injecting a control mask into decoding to prune red- flag continuations; the implementation may include a tire of banned token sequence or a classifier-guided decoding term that may penalize unsafe trajectory; the specific technology improved may be safe inference control.

[0399] In some embodiments, a repeat parameter may be set to cause a controlled repetition of the simulation and an aggregation routine may produce an aggregate transcript data so that stability may be quantified; the technical problem of high-variance output may be addressed by running N repeated trials under fixed seed strata and by computing a statistic such as variance of adherence and mean time-to-completion, with the aggregate stored alongside the report; the implementation may include stratified sampling across twin parameters or across stochastic temperature; the specific technology improved may be statistical reliability measurement for dialog systems.

[0400] In some embodiments, a bias indicator set may be defined and a bias score may be generated by scanning a transcript for proxy markers and asymmetric failure; the technical problem of hidden demographic bias may be addressed by aligning twin parameter to sensitive attribute proxies and by computing disparity in constraint-violation rate; the implementation may include lexicon-based cue detection or an embeddingdistance probe with permutation test; the specific technology improved may be fairness assessment and governance for conversational Al.

[0401] In some embodiments, a counterfactual simulator may be derived from a patient twin by perturbing one feature at a time to assess sensitivity of protocol adherence and content safety to that feature; the technical problem of unobserved confounder in evaluation may be addressed by generating a matched pair of transcripts that may differ only in a controlled feature and by computing a counterfactual risk delta; the implementation may include a differentiable perturbation of the twin embedding or a rule-based swap in the typed feature map; the specific technology improved may be causal evaluation of dialog behavior.

[0402] In some embodiments, an uncertainty estimator may be integrated to output a confidence measure per generated statement so that downstream rendering may suppress low-confidence content; the technical problem of over-confident wrong statements may be addressed by using entropy of the token distribution, disagreement of an ensemble head, or conformal prediction over n-best responses; the implementation may include Monte-Carlo dropout at decoding or score calibration on a validation set; the specific technology improved may be calibrated generation for clinical dialog.

[0403] In some embodiments, a federated adaptation module may be added to fine-tune a response rule across institution silo without centralizing raw transcript; the technical problem of cross-site generalization under privacy constraint may be addressed by orchestrating a parameter delta exchange that may be aggregated with secure aggregation so that a global adapter may be produced; the module may include client-side gradient clipping and server-side differential privacy noise so that update leakage risk may be reduced; the specific technology improved may be privacy-preserving model training.

[0404] In some embodiments, a protocol specification language may be introduced so that a protocol may be authored as an executable specification that may compile into a state machine, a constraint set, and a report schema; the technical problem of ambiguous narrative protocol text may be addressed by defining typed action, slot, and guard constructs and by providing a compiler that may emit validators and constrained- decoding hooks; the language may include an inheritance mechanism so that a sitespecific amendment may extend a base protocol; the specific technology improved may be formal verification and tool-assisted compliance.

[0405] In some embodiments, a retrieval- augmented twin may be employed so that up-to- date drug label and guideline content may be cited inside a constrained response without retraining a base model; the technical problem of stale parametric knowledge may be addressed by indexing a vetted corpus and by performing query construction from protocol state and twin feature so that a response may condition on retrieved passages; the implementation may include passage hashing and provenance tagging so that each assertion may link to a source; the specific technology improved may be knowledge freshness and traceability in generation.

[0406] In some embodiments, a confidential-compute deployment may be configured so that transcript processing and twin parameter update may occur inside a secure enclave with remote attestation; the technical problem of data exposure on a multi-tenant platform may be addressed by sealing keys to enclave measurement and by restricting decryption of a transcript to an attested execution; the implementation may be based on an enclave abstraction that may guard a de-identification routine and a report Tenderer; the specific technology improved may be runtime data protection for cloud inference.

[0407] In some embodiments, a cryptographic audit log may be integrated so that each simulation step, constraint decision, and report field may be recorded as a hash-chained entry with a timestamp and a signature; the technical problem of post-hoc tamper detection and provenance reconstruction may be addressed by storing an append-only log that may anchor a digest to a trusted timestamp service; the implementation may include per-entry Merkle proof so that field-level verification may be supported; the specific technology improved may be auditability and forensic accountability.

[0408] In some embodiments, an adversarial assessment harness may be added so that an automatic red-team prompt may challenge a protocol guard with near-miss attacks aimed at eliciting unsafe or off-protocol content; the technical problem of brittleness to promptlevel attacks may be addressed by generating attack variants with gradient- free search over instruction phrasing and by measuring failure surface under the constraint engine; the harness may record failure and may synthesize a new rule to block the discovered pattern; the specific technology improved may be robustness of guarded generation.

[0409] In some embodiments, a latency-aware scheduler may be introduced so that constrained decoding and validation may be interleaved with low-latency shortcuts under a tight service-level target; the technical problem of worst-case delay due to heavy validation may be addressed by predicting a low-risk path for trivial turns and by applying a fast constraint check, while reserving full validation for high-risk turns that may be detected by a risk classifier; the implementation may include budgeted beam search that may terminate early under high confidence; the specific technology improved may be real-time responsiveness of constrained dialog.

[0410] In some embodiments, a synthetic-data generator with differential privacy may be used to produce a shareable transcript corpus that may retain utility for model evaluation without exposing a sensitive pattern; the technical problem of scarce sharable clinical dialog data may be addressed by sampling from the simulator under a privacy accountant and by measuring privacy loss with a composition bound; the implementation may include posterior sampling with noise addition to slot values and a membership-inference audit; the specific technology improved may be safe data sharing and reproducible benchmarking. In some embodiments, a multi-objective controller may be added so that optimization may balance protocol adherence, safety, empathy style of the twin, and latency; the technical problem of single-objective tuning that may degrade a secondary objective may be addressed by defining a scalarized objective with tunable weight and by learning a policy that may control decoding temperature, beam width, and constraint tightness per state; the specific technology improved may be controllable generation under operational constraints

[0411] FIG. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform 100 may be hosted on a centralized server 102, such as, for example, a cloud computing service. The centralized server 102 may communicate with other network entities, such as, for example, a mobile device 106 (such as a smartphone, a laptop, a tablet computer etc.), other electronic devices 110 (such as desktop computers, server computers etc.), databases 114, and sensors 116 over a communication network 104, such as, but not limited to, the Internet. Further, users of the online platform 100 may include relevant parties such as, but not limited to, end-users, administrators, service providers, service consumers and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

[0412] A user 112, such as the one or more relevant parties, may access online platform 100 through a web based software application or browser. The web based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 200.

[0413] With reference to FIG. 2, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device 200. In a basic configuration, computing device 200 may include at least one processing unit 202 and a system memory 204. Depending on the configuration and type of computing device, system memory 204 may comprise, but is not limited to, volatile (e.g. randomaccess memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory 204 may include operating system 205, one or more programming modules 206, and may include a program data 207. Operating system 205, for example, may be suitable for controlling computing device 200’ s operation. In one embodiment, programming modules 206 may include image -processing module, machine learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 2 by those components within a dashed line 208.

[0414] Computing device 200 may have additional features or functionality. For example, computing device 200 may also include additional data storage devices (removable and / or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 2 by a removable storage 209 and a nonremovable storage 210. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory 204, removable storage 209, and non-removable storage 210 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable readonly memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 200. Any such computer storage media may be part of device 200. Computing device 200 may also have input device(s) 212 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s) 214 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

[0415] Computing device 200 may also contain a communication connection 216 that may allow device 200 to communicate with other computing devices 218, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 216 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

[0416] As stated above, a number of program modules and data files may be stored in system memory 204, including operating system 205. While executing on processing unit 202, programming modules 206 (e.g., application 220 such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit 202 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.

[0417] Generally, consistent with embodiments of the disclosure, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the disclosure may be practiced with other computer system configurations, including hand-held devices, general purpose graphics processor-based systems, multiprocessor systems, microprocessor-based or programmable consumer electronics, application specific integrated circuit-based electronics, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems.

[0418] Embodiments of the disclosure, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present disclosure may be embodied in hardware and / or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present disclosure may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0419] The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0420] Embodiments of the present disclosure, for example, are described above with reference to block diagrams and / or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions / acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality / acts involved.

[0421] While certain embodiments of the disclosure have been described, other embodiments may exist. Furthermore, although embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, solid state storage (e.g., USB drive), or a CD- ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods’ stages may be modified in any manner, including by reordering stages and / or inserting or deleting stages, without departing from the disclosure.

[0422] Fig. 3A and Fig. 3B illustrate a flowchart of a method 300 of automated clinical study simulation, in accordance with some embodiments.

[0423] Accordingly, the method 300 may include a step 302 of receiving, using a communication device 702, one or more profile data from one or more client devices. Further, the one or more profile data may be associated with one or more users. Further, the method 300 may include a step 304 of generating, using a processing device 704, a digital twin data based on the one or more profile data. Further, the digital twin data corresponds to a digital representation of the one or more users. Further, the method 300 may include a step 306 of generating, using the processing device 704, one or more user inputs for an Al model using the digital twin data. Further, the method 300 may include a step 308 of obtaining, using the processing device 704, one or more response data from the Al model. Further, the Al model may be configured to generate the one or more response data for the one or more user inputs. Further, the method 300 may include a step 310 of analyzing, using the processing device 704, the one or more user inputs and the one or more response data. Further, the method 300 may include a step 312 of generating, using the processing device 704, one or more result data based on the analyzing. Further, the one or more result data includes a result associated with the validation of the performance of the Al model. Further, the method 300 may include a step 314 of transmitting, using the communication device 702, the one or more result data to the one or more client devices.

[0424] In some embodiments, the generating of the one or more result data includes computing one or more performance metrics of the Al model using an analytic generator based on the analyzing of the one or more user inputs and the one or more response data. Further, the one or more result data includes the one or more performance metrics.

[0425] In some embodiments, the analyzing of the one or more user inputs and the one or more response data includes analyzing the one or more response data for one or more bias indicators. Further, the generating of the one or more result data includes generating a bias score of the Al model based on the analyzing of the one or more response data for one or more bias indicators. Further, the one or more result data includes the bias score of the Al model.

[0426] In some embodiments, the method 300 may be embodied as instructions stored on a non-transitory computer-readable medium that, when executed by the processing device 704, cause the processing device 704 to perform the method 300.

[0427] Fig. 4 illustrates a flowchart of a method 400 of automated clinical study simulation including generating, using the processing device 704, at least one rule for generating the at least one user input based determining of the at least one characteristic, in accordance with some embodiments. Further, in some embodiments, the method 400 further may include a step 402 of analyzing, using the processing device 704, the one or more profile data. Further, in some embodiments, the method 400 further may include a step 404 of determining, using the processing device 704, one or more characteristics of the one or more users based on the analyzing of the one or more profile data. Further, in some embodiments, the method 400 further may include a step 406 of generating, using the processing device 704, one or more rules for generating the one or more user input based determining of the one or more characteristics. Further, the generating of the one or more user inputs may be further based on the generating of the one or more rules. Further, the generating of the one or more user inputs includes generating the one or more user inputs using the digital twin data based on the one or more rules.

[0428] In some embodiments, the method 300 may further include receiving, using the communication device 702, one or more validation requests from the one or more client devices. Further, the one or more validation requests indicates one or more interaction stages. Further, the generating of the one or more user inputs may be further based on the one or more validation requests. Further, the generating of the one or more user inputs includes generating the one or more user inputs based on the one or more interaction stages using the digital twin data. Further, the one or more user inputs may be configured for evaluating one or more aspects of the Al model associated with the one or more interaction stages.

[0429] In some embodiments, the one or more interaction stages include two or more interaction stages. Further, the generating of the one or more user inputs includes generating two or more user inputs by progressing the two or more interaction stages from a deterministic interaction stage to a non-deterministic interaction stage.

[0430] Fig. 5 illustrates a flowchart of a method 500 of automated clinical study simulation including determining, using the processing device 704, a stability metric of the digital twin, in accordance with some embodiments.

[0431] Further, in some embodiments, the method 500 further may include a step 502 of aggregating, using the processing device 704, two or more user inputs associated with the digital twin data. Further, the two or more user inputs may be associated with two or more conversation interactions. Further, in some embodiments, the method 500 further may include a step 504 of analyzing, using the processing device 704, the two or more user inputs. Further, in some embodiments, the method 500 further may include a step 506 of determining, using the processing device 704, a stability metric of the digital twin based on the analyzing the two or more user inputs. Further, the generating of the one or more result data may be further based on the determining of the stability metric. Further, the one or more result data includes the stability metric.

[0432] In some embodiments, the method 300 may further include generating, using the processing device 704, a feedback data based on the analyzing of the one or more user inputs and the one or more response data. Further, the Al model may be configured to improve one or more aspects of the Al model based on the feedback data.

[0433] In some embodiments, the validation of the performance of the Al model may be associated with one or more studies. Further, the one or more studies may be based on one or more protocols. Further, the method 300 further includes receiving, using the communication device 702, a protocol data from the one or more client devices. Further, the one or more protocol data includes the one or more protocols. Further, the generating of the one or more user inputs may be further based on the protocol data. Further, the analyzing of the one or more user inputs and the one or more response data may be further based on the protocol data; determining, using the processing device 704, one or more deviations in the one or more user inputs in relation to the one or more protocols based on the analyzing of the one or more user inputs and the one or more response data; and updating, using the processing device 704, the digital twin data of the one or more users based on the one or more deviations.

[0434] Fig. 6 illustrates a flowchart of a method 600 of automated clinical study simulation encrypting, using the processing device 704, at least one of the at least one user input and the at least one response data, in accordance with some embodiments.

[0435] Further, in some embodiments, the method 600 further may include a step 602 of identifying, using the processing device 704, one or more personal information in one or more of the one or more user inputs and the one or more response data based on the analyzing of the one or more user inputs and the one or more response data. Further, in some embodiments, the method 600 further may include a step 604 of encrypting, using the processing device 704, one or more of the one or more user inputs and the one or more response data based on the identifying to obtain one or more of an encrypted input data and an encrypted response data.

[0436] Fig. 7 illustrates a block diagram of a system 700 for automated clinical study simulation, in accordance with some embodiments.

[0437] Accordingly, the system 700 may include a communication device 702. Further, the communication device 702 may be configured for receiving one or more profile data from one or more client devices. Further, the one or more profile data may be associated with one or more users. Further, the communication device 702 may be configured for transmitting one or more result data to the one or more client devices. Further, the system 700 may include a processing device 704 communicatively coupled with the communication device 702. Further, the processing device 704 may be configured for generating a digital twin data based on the one or more profile data. Further, the digital twin data corresponds to a digital representation of the one or more users. Further, the processing device 704 may be configured for generating one or more user inputs for an Al model using the digital twin data. Further, the processing device 704 may be configured for obtaining one or more response data from the Al model. Further, the Al model may be configured to generate the one or more response data for the one or more user inputs. Further, the processing device 704 may be configured for analyzing the one or more user inputs and the one or more response data. Further, the processing device 704 may be configured for generating the one or more result data based on the analyzing. Further, the one or more result data includes a result associated with the validation of the performance of the Al model.

[0438] In some embodiments, the generating of the one or more result data includes computing one or more performance metrics of the Al model using an analytic generator based on the analyzing of the one or more user inputs and the one or more response data. Further, the one or more result data includes the one or more performance metrics. In some embodiments, the analyzing of the one or more user inputs and the one or more response data includes analyzing the one or more response data for one or more bias indicators. Further, the generating of the one or more result data includes generating a bias score of the Al model based on the analyzing of the one or more response data for one or more bias indicators. Further, the one or more result data includes the bias score of the Al model.

[0439] Further, in some embodiments, the processing device 704 may be further configured for analyzing the one or more profile data. Further, the processing device 704 may be further configured for determining one or more characteristics of the one or more users based on the analyzing of the one or more profile data. Further, the processing device 704 may be further configured for generating one or more rules for generating the one or more user input based determining of the one or more characteristics. Further, the generating of the one or more user inputs may be further based on the generating of the one or more rules. Further, the generating of the one or more user inputs includes generating the one or more user inputs using the digital twin data based on the one or more rules.

[0440] In some embodiments, the communication device 702 may be further configured for receiving one or more validation requests from the one or more client devices. Further, the one or more validation requests indicates one or more interaction stages. Further, the generating of the one or more user inputs may be further based on the one or more validation requests. Further, the generating of the one or more user inputs includes generating the one or more user inputs based on the one or more interaction stages using the digital twin data. Further, the one or more user inputs may be configured for evaluating one or more aspects of the Al model associated with the one or more interaction stages.

[0441] In some embodiments, the one or more interaction stages includes two or more interaction stages. Further, the generating of the one or more user inputs includes generating two or more user inputs by progressing the two or more interaction stages from a deterministic interaction stage to a non-deterministic interaction stage. Further, in some embodiments, the processing device 704 may be further configured for aggregating two or more user inputs associated with the digital twin data. Further, the two or more user inputs may be associated with two or more conversation interactions. Further, the processing device 704 may be further configured for analyzing the two or more user inputs. Further, the processing device 704 may be further configured for determining a stability metric of the digital twin based on the analyzing the two or more user inputs. Further, the generating of the one or more result data may be further based on the determining of the stability metric. Further, the one or more result data includes the stability metric.

[0442] In some embodiments, the processing device 704 may be further configured for generating a feedback data based on the analyzing of the one or more user inputs and the one or more response data. Further, the Al model may be configured to improve one or more aspects of the Al model based on the feedback data.

[0443] Further, in some embodiments, the validation of the performance of the Al model may be associated with one or more studies. Further, the one or more studies may be based on one or more protocols. Further, the communication device 702 may be further configured for receiving a protocol data from the one or more client devices. Further, the generating of the one or more user inputs may be further based on the protocol data. Further, the analyzing of the one or more user inputs and the one or more response data may be further based on the protocol data. Further, the processing device 704 may be further configured for determining one or more deviations in the one or more user inputs in relation to the one or more protocols based on the analyzing of the one or more user inputs and the one or more response data. Further, the validation of the performance of the Al model may be associated with one or more studies. Further, the generating of the one or more user inputs may be further based on the protocol data. Further, the processing device 704 may be further configured for updating the digital twin data of the one or more users based on the one or more deviations.

[0444] Further, in some embodiments, the processing device 704 may be further configured for identifying one or more personal information in one or more of the one or more user inputs and the one or more response data based on the analyzing of the one or more user inputs and the one or more response data. Further, the processing device 704 may be further configured for encrypting one or more of the one or more user inputs and the one or more response data based on the identifying to obtain one or more of an encrypted input data and an encrypted response data.

[0445] In some embodiments, the two or more interaction stages correspond to a deterministic interaction, a semi-deterministic interaction, a guided conversational interaction, a dynamic conversational interaction, a patient-specific conversational interaction, a realistic scenario conversational interaction, and a full-scale conversational interaction.

[0446] In some embodiments, the progressing of the two or more user inputs includes progressing the two or more user inputs through the two or more interaction stages using one or more mode schedulers. Further, the one or more mode schedulers may be configured to progress the conversation interaction based on one or more stage criteria indicated in one or more protocols.

[0447] In some embodiments, the generating of the one or more user inputs includes generating the one or more user inputs using the digital twin data in accordance with the one or more protocols.

[0448] In some embodiments, the method 300 may further include generating, using the processing device 704, one or more rule data based on the protocol data using a protocol- compiled constraint engine. Further, the one or more rule data includes one or more rules associated with the generating of the one or more user inputs. Further, the generating of the one or more user inputs may be further based on the one or more rules.

[0449] In some embodiments, the one or more rule data includes one or more of a finite-state control rule and a token-level rule.

[0450] In some embodiments, the digital twin data corresponds to a compositional model which may be configured for utilizing one or more knowledge graphs and one or more parameter vectors. Further, the one or more knowledge graphs include one or more nodes corresponding to one or more user entities and the one or more parameter vectors indicate the one or more characteristics of the one or more users.

[0451] In some embodiments, the method 300 may further include extracting, using the processing device 704, one or more format rules from the one or more protocols. Further, the generating of the one or more result data may be further based on the extracting of the one or more format rules. Further, the one or more result data may be compliant with the one or more format rules.

[0452] Fig. 8 illustrates a flowchart of a method 800 of automated clinical study simulation including repeating, using the processing device 704, the generating of the at least one user input, in accordance with some embodiments.

[0453] Further, in some embodiments, the method 800 further may include a step 802 of establishing, using the processing device 704, a repeat parameter for the conversation interaction. Further, in some embodiments, the method 800 further may include a step 804 of repeating, using the processing device 704, the generating of the one or more user inputs based on the repeating parameter. Further, the generating of the one or more result data may be further based on the repeating of the simulating of the conversational interaction.

[0454] In some embodiments, the one or more studies include one or more clinical studies. Further, the one or more profile data includes one or more medical histories of the one or more users. Further, the digital twin data may be associated with one or more characteristics of the one or more users. Further, the one or more characteristics include one or more of a behavioral pattern, a symptom associated with one or more medical conditions of the one or more uses.

[0455] In some embodiments, the Al model includes a deterministic conversational model and a non-deterministic conversational model.

[0456] In some embodiments, the one or more user inputs and the one or more response data correspond to a deterministic question and answer exchange. In some embodiments, the Al model includes a large language model. Further, one or more user inputs and the one or more response data include a dynamic conversation.

[0457] In some embodiments, the method 600 may further include replacing, using the processing device 704, the one or more personal information with one or more tokens based on the identifying of the one or more personal information. Further, the encrypting of one or more of the one or more user inputs and the one or more response data may be further based on the replacing of the one or more personal information. Further, one or more of the encrypted input data and the encrypted response data further include the one or more tokens for the one or more personal information.

[0458] In some embodiments, the one or more result data includes a comprehensive report of the one or more studies for publication.

[0459] In some embodiments, the encrypting of one or more of the one or more user inputs and the one or more response data includes encrypting at least one of the one or more user inputs and the one or more response data in accordance with one or more criteria. Further, the one or more criteria include one or more regulatory criteria for protecting the one or more personal information of the one or more users.

[0460] In some embodiments, the one or more studies may be associated with one or more of a healthcare industry, a finance industry, an education industry, and a retail industry.

[0461] In some embodiments, the one or more protocols include one or more clinical study protocols.

[0462] In some embodiments, the generating of the digital twin data includes generating the digital twin data by following one or more ethical guidelines.

[0463] In some embodiments, the generating of the one or more result data includes generating the one or more result data using the digital twin data by following the one or more ethical guidelines.

[0464] In some embodiments, the one or more guidelines correspond to one or more of an informed consent from the one or more users and a data privacy consideration. In some embodiments, the digital twin data corresponds to one or more of a virtual twin and a physical twin. Further, the physical twin includes one or more of an android and a robot. Further, the physical twin may be configured to simulate one or more characteristics comprising one or more of a user behavior and a user response.

[0465] In some embodiments, the one or more regulatory criteria include one or more of a Health Insurance Portability and Accountability Act (HIPAA), a Personal Information Protection Act Electronic Document Act (PIPEDA), and a General Data Protection Regulation (GDPR).

[0466] In some embodiments, the analytic generator may be configured to generate the one or more performance metrics based on one or more of a performance of the Al model, a quality of the conversation interaction, and a user satisfaction.

[0467] In some embodiments, the analytic generator includes a machine learning model (ML) model.

[0468] In some embodiments, the two or more interaction stages may be associated with two or more complexity levels.

[0469] In some embodiments, the generating of the feedback data includes generating the feedback data using a second Al model. Further, the second Al model may be configured to optimize one or more conversational capabilities of the Al model.

[0470] In some embodiments, the one or more profile data includes an Electronic Health Record (EHR) of the one or more users.

[0471] In some embodiments, the digital representation of the one or more users corresponds to a behavioral representation of the one or more users.

[0472] Fig. 9 is a block diagram of a system 900 for facilitating simulating automated clinical study, in accordance with some embodiments.

[0473] Accordingly, the system 900 may include a processing device 904 configured for obtaining at least one digital twin data. Further, the at least one digital twin data may include a range of clinical scenarios. Further, the range of clinical scenarios may include specific medical histories, symptoms, and behavioral patterns.

[0474] Further, the processing device 904 may be configured for generating at least one digital twin based on the at least one digital twin data.

[0475] Further, the processing device 904 may be configured for processing at least one interaction input and the at least one digital twin data based on at least one clinical study protocol. Further, the processing device 904 may be configured for determining at least one interaction outcome based on the processing. Further, a method 1000 may include analyzing, using the processing device 904, the at least one interaction outcome using at least one artificial intelligence model. Further, the processing device 904 may be configured for generating at least one report using an integrated reporting engine.

[0476] Further, the system 900 may include a communication device 902 configured for transmitting the at least one digital twin to at least one presentation device. Further, the at least one presentation device may include a projector, an LED display device, etc. Further, the communication device 902 may be configured for receiving the at least one interaction input associated with at least one interaction from at least one device. Further, the at least one device may include at least one user device associated with a healthcare professional (such as a caretaker, a nurse, a doctor, a surgeon, etc.). Further, the at least one user device may include a smartphone, a tablet, a laptop, and so on. Further, the at least one interaction input may include a deterministic input and a non-deterministic input. Further, the communication device 902 may be configured for transmitting the at least one report to the at least one user device.

[0477] Further, the system 900 may include a storage device 906 configured for storing at least one of the at least one report, the at least one interaction outcome, and the at least one digital data based on at least one medical guideline. Further, the at least one medical guideline may be provided by at least one authority. Further, the at least one medical guideline may include regulatory requirements such as HIPAA, PIPEDA, and GDPR. Fig. 10 is a flow chart of a method 1000 for facilitating simulating an automated clinical study, in accordance with some embodiments.

[0478] Accordingly, the method 1000 may include a step 1002 of obtaining, using a processing device 904, at least one digital twin data. Further, the at least one digital twin data may include a range of clinical scenarios. Further, the range of clinical scenarios may include specific medical histories, symptoms, and behavioral patterns.

[0479] Further, the method 1000 may include a step 1004 of generating, using the processing device 904, at least one digital twin based on the at least one digital twin data.

[0480] Further, the method 1000 may include a step 1008 of transmitting, using a communication device, the at least one digital twin to at least one presentation device. Further, the at least one presentation device may include a projector, an LED display device, etc. Further, the at least one presentation device may be configured for presenting the at least one digital twin.

[0481] Further, the method 1000 may include a step 1010 of receiving, using the communication device 902, at least one interaction input associated with at least one interaction from at least one device. Further, the at least one device may include at least one user device associated with a healthcare professional (such as a caretaker, a nurse, a doctor, a surgeon, etc.). Further, the at least one user device may include a smartphone, a tablet, a laptop, and so on. Further, the at least one interaction input may include a deterministic input and a non-deterministic input. Further, the at least one device may include an input device (such as a sensor comprising an image sensor, a movement sensor, etc.) configured for capturing at least one action that may be performed on the at least one digital twin by the healthcare professional. Further, the at least one interaction input may be in accordance with a clinical trial and / or experiment.

[0482] Further, the method 1000 may include a step 1010 of processing, using the processing device 904, the at least one interaction input and the at least one digital twin data based on at least one clinical study protocol. Further, the method 1000 may include a step 1012 of determining, using the processing device 904, at least one interaction outcome based on the processing. Further, the method 1000 may include a step 1014 of analyzing, using the processing device 904, the at least one interaction outcome using at least one artificial intelligence model.

[0483] Further, the method 1000 may include a step 1016 of generating, using the processing device 904, at least one report using an integrated reporting engine.

[0484] Further, the method 1000 may include a step 1018 of transmitting, using the communication device 902, the at least one report to the at least one user device.

[0485] Further, the method 1000 may include a step 1020 of storing, using a storage device 906, at least one of the at least one report, the at least one interaction outcome, and the at least one digital data based on at least one medical guideline. Further, the at least one medical guideline may be provided by at least one authority. Further, the at least one medical guideline may include regulatory requirements such as HIPAA, PIPEDA, and GDPR.

[0486] Further, in some embodiments, the method 1000 may include receiving, using the communication device 902, at least one feedback from the at least one user device. Further, the method 1000 may include updating, using the processing device 904, the at least one digital twin based on the at least one feedback. Further, the method 1000 may include generating, using the processing device 904, at least one updated digital twin based on the updating. Further, the transmitting of the at least one digital twin may include transmitting the at least one updated digital twin.

[0487] Although the invention has been explained in relation to its preferred embodiment, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the invention as hereinafter claimed.

Claims

CLAIMS1. A method of automated clinical study simulation, the method comprising: receiving, using a communication device, at least one profile data from at least one client device, wherein the at least one profile data is associated with at least one user; generating, using a processing device, a digital twin data based on the at least one profile data, wherein the digital twin data corresponds to a digital representation of the at least one user; generating, using the processing device, at least one user input for the Al model using the digital twin data; obtaining, using the processing device, at least one response data from the Al model, wherein an Al model is configured to generate the at least one response data for the at least one user input; analyzing, using the processing device, the at least one user input and the at least one response data; generating, using the processing device, at least one result data based on the analyzing, wherein the at least one result data comprises a result associated with the validation of the performance of the Al model; and transmitting, using the communication device, the at least one result data to the at least one client device.

2. The method of claim 1 , wherein the generating of the at least one result data comprises computing at least one performance metric of the Al model using an analytic generator based on the analyzing of the at least one user input and the at least one response data, wherein the at least one result data comprises the at least one performance metric.

3. The method of claim 1, wherein the analyzing of the at least one user input and the at least one response data comprises analyzing the at least one response data for at least one bias indicator, wherein the generating of the at least one result data comprises generatinga bias score of the Al model based on the analyzing of the at least one response data for at least one bias indicator, wherein the at least one result data comprises the bias score of the Al model.

4. The method of claim 1 further comprising: analyzing, using the processing device, the at least one profile data; determining, using the processing device, at least one characteristic of the at least one user based on the analyzing of the at least one profile data; and generating, using the processing device, at least one rule for generating the at least one user input based determining of the at least one characteristic, wherein the generating of the at least one user input is further based on the generating of the at least one rule, wherein the generating of the at least one user input comprises generating the at least one user input using the digital twin data based on the at least one rule.

5. The method of claim 1 further comprising receiving, using the communication device, at least one validation request from the at least one client device, wherein the at least one validation request indicates at least one interaction stage, wherein the generating of the at least one user input is further based on the at least one validation request, wherein the generating of the at least one user input comprises generating the at least one user input based on the at least one interaction stage using the digital twin data, wherein the at least one user input is configured for evaluating at least one aspect of the Al model associated with the at least one interaction stage.

6. The method of claim 5, wherein the at least one interaction stage comprises a plurality of interaction stages, wherein the generating of the at least one user input comprises generating a plurality of user inputs by progressing the plurality of interaction stages from a deterministic interaction stage to a non-deterministic interaction stage.

7. The method of claim 1 further comprising:aggregating, using the processing device, a plurality of user inputs associated with the digital twin data, wherein the plurality of user inputs is associated with a plurality of conversation interactions; analyzing, using the processing device, the plurality of user inputs; and determining, using the processing device, a stability metric of the digital twin based on the analyzing the plurality of user inputs, wherein the generating of the at least one result data is further based on the determining of the stability metric, wherein the at least one result data comprises the stability metric.

8. The method of claim 1 further comprising generating, using the processing device, a feedback data based on the analyzing of the at least one user input and the at least one response data, wherein the Al model is configured to improve at least one aspect of the Al model based on the feedback data.

9. The method of claim 1, wherein the validation of the performance of the Al model is associated with at least one study, wherein the at least one study is based on at least one protocol, wherein the method further comprises: receiving, using the communication device, a protocol data from the at least one client device, wherein the at least one protocol data comprises the at least one protocol, wherein the generating of the at least one user input is further based on the protocol data, wherein the analyzing of the at least one user input and the at least one response data is further based on the protocol data; determining, using the processing device, at least one deviation in the at least one user input in relation to the at least one protocol based on the analyzing of the at least one user input and the at least one response data; and updating, using the processing device, the digital twin data of the at least one user based on the at least one deviation.

10. The method of claim 1 further comprising:identifying, using the processing device, at least one personal information in at least one of the at least one user input and the at least one response data based on the analyzing of the at least one user input and the at least one response data; and encrypting, using the processing device, at least one of the at least one user input and the at least one response data based on the identifying to obtain at least one of an encrypted input data and an encrypted response data.

11. A system for automated clinical study simulation, the system comprising: a communication device configured for: receiving at least one profile data from at least one client device, wherein the at least one profile data is associated with at least one user; transmitting at least one result data to the at least one client device; a processing device communicatively coupled with the communication device, wherein the processing device is configured for: generating a digital twin data based on the at least one profile data, wherein the digital twin data corresponds to a digital representation of the at least one user; generating at least one user input for an Al model using the digital twin data; obtaining at least one response data from the Al model, wherein the Al model is configured to generate the at least one response data for the at least one user input; analyzing the at least one user input and the at least one response data; and generating the at least one result data based on the analyzing, wherein the at least one result data comprises a result associated with the validation of the performance of the Al model.

12. The system of claim 11, wherein the generating of the at least one result data comprises computing at least one performance metric of the Al model using an analytic generator based on the analyzing of the at least one user input and the at least one response data, wherein the at least one result data comprises the at least one performance metric.

13. The system of claim 11, wherein the analyzing of the at least one user input and the at least one response data comprises analyzing the at least one response data for at least one bias indicator, wherein the generating of the at least one result data comprises generating a bias score of the Al model based on the analyzing of the at least one response data for at least one bias indicator, wherein the at least one result data comprises the bias score of the Al model.

14. The system of claim 11, wherein the processing device is further configured for: analyzing the at least one profile data; determining at least one characteristic of the at least one user based on the analyzing of the at least one profile data; and generating at least one rule for generating the at least one user input based determining of the at least one characteristic, wherein the generating of the at least one user input is further based on the generating of the at least one rule, wherein the generating of the at least one user input comprises generating the at least one user input using the digital twin data based on the at least one rule.

15. The system of claim 11, wherein the communication device is further configured for receiving at least one validation request from the at least one client device, wherein the at least one validation request indicates at least one interaction stage, wherein the generating of the at least one user input is further based on the at least one validation request, wherein the generating of the at least one user input comprises generating the at least one user input based on the at least one interaction stage using the digital twin data, wherein the at least one user input is configured for evaluating at least one aspect of the Al model associated with the at least one interaction stage.

16. The system of claim 15, wherein the at least one interaction stage comprises a plurality of interaction stages, wherein the generating of the at least one user input comprises generating a plurality of user inputs by progressing the plurality of interaction stages from a deterministic interaction stage to a non-deterministic interaction stage.

17. The system of claim 11, wherein the processing device is further configured for: aggregating a plurality of user inputs associated with the digital twin data, wherein the plurality of user inputs is associated with a plurality of conversation interactions; analyzing the plurality of user inputs; and determining a stability metric of the digital twin based on the analyzing the plurality of user inputs, wherein the generating of the at least one result data is further based on the determining of the stability metric, wherein the at least one result data comprises the stability metric.

18. The system of claim 11, wherein the processing device is further configured for generating a feedback data based on the analyzing of the at least one user input and the at least one response data, wherein the Al model is configured to improve at least one aspect of the Al model based on the feedback data.

19. The system of claim 11, wherein the validation of the performance of the Al model is associated with at least one study, wherein the at least one study is based on at least one protocol, wherein the communication device is further configured for receiving a protocol data from the at least one client device, wherein the at least one protocol data comprises the at least one protocol, wherein the generating of the at least one user input is further based on the protocol data, wherein the analyzing of the at least one user input and the at least one response data is further based on the protocol data, wherein the processing device is further configured for: determining at least one deviation in the at least one user input in relation to the at least one protocol based on the analyzing of the at least one user input and the at least one response data; andupdating the digital twin data of the at least one user based on the at least one deviation.

20. The system of claim 11 , wherein the processing device is further configured for: identifying at least one personal information in at least one of the at least one user input and the at least one response data based on the analyzing of the at least one user input and the at least one response data; and encrypting at least one of the at least one user input and the at least one response data based on the identifying to obtain at least one of an encrypted input data and an encrypted response data.

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