System for real-time drug interaction alerts through AI-assisted analysis

Abstract

A real-time drug interaction alert system using AI-powered analytics, consisting of: a wearable or implantable biosensor device configured to continuously measure physiological parameters of a subject, including at least heart rate, blood pressure, blood oxygen saturation, skin temperature, and biomarkers related to liver and kidney function; a communication module integrated into the device that can securely transmit physiological data to a cloud-based analysis engine via wireless protocols such as Bluetooth Low Energy (BLE), Wi-Fi, or LTE-M; a cloud-hosted artificial intelligence (AI) inference engine configured to perform multimodal drug-drug interaction risk analysis, the engine comprising at least a convolutional neural network (CNN) for time-series analysis of biosignal patterns, a natural language processing (NLP) module for extracting drug information from structured and unstructured medical sources, and a Bayesian reasoning module for modeling probabilistic risk interactions between drugs and patient-specific variables; a pharmaceutical input interface for capturing medication data by at least one of the following methods: manual entry, optical character recognition (OCR) of the packaging, or near field communication (NFC) scanning of embedded tags; an alert dissemination module configured to trigger multi-channel alerts, including haptic signals via the wearable device, audiovisual notifications via a paired smart device, and the secure transmission of interaction reports to designated healthcare professionals based on severity thresholds.

Description

Field of the InventionThe present invention relates to the fields of biomedical monitoring, artificial intelligence in healthcare and pharmaceutical safety systems. More specifically, it is a smart real-time system for detecting and alerting potentially deleterious drug interactions through the integration of wearable biosensor technology, AI-based context inference, and secure communication architectures.BACKGROUND OF THE INVENTIONThe increasing spread of polypharmaceuticals, where patients take multiple drugs simultaneously, has significantly increased the risk of undesirable drug interactions (ADIs). This challenge is particularly acute among elderly people, patients with chronic diseases and persons undergoing complex therapy regimens. Conventional drug interaction verification systems, which typically rely on static databases or the consulting of pharmaceuticals, lack personalization, context awareness, and real-time adaptation. In addition, such systems often account for non-patient specific physiological parameters, genetic predispositions, or environmental factors that may affect pharmacokinetics and pharmacodynamics. This leads to the failure to recognize numerous avoidable complications such as liver toxicity, kidney toxicity, QT prolongation and synergistic CNS depression or to identify them only after clinical manifestation.Advances in wearable health care, machine learning, and ubiquitous computing disclose the ability to develop a smart system that not only recognizes potential interactions of drugs, but also adjusts to each patient's individual physiological and pharmacological profile in real-time. Despite the availability of clinical decision support systems (CDSS), only few existing technologies integrate biosensor data, real-time AI analyses, and personalized health records into a conclusive, automated solution. Therefore, there is a need for a robust, AI-based system that can continuously monitor, predict, and alert users of possible harmful interactions of drugs with minimal manual effort, thus ensuring safety in various health environments.Drug administration has become increasingly complex in modern medicine due to the widespread polypharmaceuticals, especially among the aging population and patients suffering from chronic conditions such as diabetes, cardiovascular diseases, neurological disorders and cancer. Although drug therapies offer considerable advantages, they also pose considerable risks when multiple drugs are applied simultaneously and without sufficient monitoring. One of the greatest concerns in this context is the occurrence of undesirable drug interactions (ADIs) which can lead to therapy failure, drug toxicity, hospitalization, and even death. Drug interactions may be pharmacokinetic in nature when one drug affects the absorption, distribution, metabolism or excretion of another, or pharmacodynamic in nature when two or more drugs produce synergistic, antagonistic or additive effects that are clinically deleterious. These interactions are often not sufficiently recognized, especially in the ambulatory and domestic care areas where real-time monitoring is minimal or absent. The consequences of such misses place a considerable burden on the health care systems, both in terms of economic costs and patient safety.Conventional approaches to managing drug interactions rely largely on static databases incorporated into electronic prescription systems or pharmacy software. These systems, such as Micromedex, Lexikor Medscape, provide tools for testing drug interactions that list potential side effects based on known interactions. However, these tools are naturally limited. They are usually based on rule-based logic and do not take into account physiological real-time data or patient-specific variability such as age, weight, kidney function, liver enzyme activity or genetic polymorphisms in drug metabolizing enzymes such as CYP293 isoforms. Thus, the alerts generated by these systems are often too general, resulting in "alert fatigue" to physicians who can be desensitized to alerts without relevance or specificity. Moreover, these systems do not operate continuously or in real time and their utility is limited to the time of prescription or delivery at the pharmacy, not during ongoing treatment or when patients administer medication themselves.Mobile applications designed for private use, such as Drugs.com, Medisafe or the interaction checker of WebTM, provide information on drug interactions in a more user-friendly format. However, these apps also rely on static inputs and do not have access to the user's physiological data or drug delivery fidelity in real time. Moreover, they must input each medication themselves to the user, thereby presenting the risk of omissions, errors or incomplete information, particularly in older users or persons with limited health competence. These applications also do not take into account interactions between non-prescription drugs, dietary supplements or foods which may significantly affect the efficacy or safety of drugs.Clinical decision support systems (CDSS) embedded in electronic patient files (EHRs) provide more integrated solutions. Some advanced EHR platforms may take into account laboratory results, comorbidities, and previous side effects in detecting drug interactions. However, the effectiveness of CDSS is limited by data silos, delayed data entry, and inconsistent interoperability between healthcare providers. These systems are not available to patients outside clinical settings and rely heavily on human interaction for interpretation and action. They rarely take into account real-time biosensor data, information on the environmental burden or behavioral factors such as sleep, liquid supply or stress, all of which can influence drug metabolism and efficacy.Wearable health devices such as smart watches and fitness trackers are becoming widely popular and can capture physiological signals such as heart rate, temperature, sleep patterns and activity level. These devices, while continuously generating biometric data, are currently operating independently of systems for monitoring drug interactions. Their main use is fitness tracking and in some cases early detection of cardiac arrhythmias or falls. They have neither the intelligence nor the functionality of correlating biosignals with specific drug ingestion patterns or of detecting newly occurring unwanted drug effects in real time. The lack of integration of portable biosensor platforms into pharmacological monitoring systems represents a considerable missed chance of using ubiquitous health data for drug safety.Recent advances in artificial intelligence (AI) and machine learning (ML) areas have enabled predictive analyses in various healthcare areas, including diagnostic imaging, genomics, and patient risk stratifying. In the field of pharmacology, KI has been used to search large sets of data to reveal previously unknown interactions between drugs or predict patient-specific responses to drugs. However, the practical use of AI-controlled systems for real-time interaction detection is still in the children's shoes. Existing AI solutions typically operate retrospectively with anonymous datasets or are used in research environments, rather than in clinical or ambulatory real world environments. In addition, many AI models lack clarity, which complicates clinical acceptance and regulatory approval.Attempts have also been made to integrate genomic data, for example from pharmacogenomic tests, into decisions for medicament prescription. Pharmacogenomics takes into account variations in genes coding for drug metabolizing enzymes, transporters or receptors and may affect the efficacy or toxicity of drugs. Although this approach is promising for a truly personalized medicine, it has no broad application for interpreting genomic data in real-time clinical scenarios due to high costs, limited insurance coverage, long test run times and lack of standardized clinical guidelines. In addition, pharmacogenomic data is only one aspect of drug response variability and must be integrated with other dynamic factors such as organ function, nutrition and drug interactions for an overall safety rating.Blockchain technology has also become established in the healthcare sector as a solution for secure data exchange and logging, including drug tracking. Some pilot projects suggest using blockchain to ensure traceability of recipes, monitor compliance with medication ingestion, and reduce delivery chain errors. Blockchain systems in this field, however, are still largely experimental and not yet closely integrated with real-time physiology monitoring or AI-based interaction prediction. Most existing blockchain applications focus on logistic transparency rather than on proactive drug flexibility.From the above evaluation it is clear that existing solutions either operate in isolation or are limited by static rules, lack of context perception and insufficient personalization. None of the currently available systems provide a fully integrated, smart, and adaptive approach that monitors the interaction between drugs and the patient's dynamic physiological state continuously and in real time. There is an unaccumbered need for a comprehensive solution that combines portable biosensors, AI-based predictive modeling, and real-time communication into a unitary ecosystem. Since current systems cannot capture and interpret biosensor data associated with drug interactions, patients are prone to avoidable complications, particularly in ambulatory care, elderly people, and rural or underprovisioned regions where direct clinical monitoring is not possible.In addition, the lack of a structural device that allows diagnostics, data visualization, and secure connectivity hinders the development of a full end-to-end system. No existing platform provides a hybrid hardware software interface that supports interaction with AI models, serves as a local processing node, enables secure user inputs, and acts as a diagnostic station for biochemical sampling tests of relevant risk factors for drug interactions. Therefore, there is a need for an innovative system that combines portable and structural hardware, AI inference functions, and seamless feedback mechanisms between user and physician to enable real-time, contextual, and patient-specific warnings of drug interactions. Such a system would fundamentally alter the paradigm of drug safety from reactive to proactive, greatly improve clinical results, reduce hospital instructions, and allow users to safely and precisely control their own pharmacotherapy.SUMMARY OF THE INVENTIONThe present invention discloses a system and associated apparatus for real-time warnings of drug interactions using AI-based analysis. The system integrates a portable or implantable biosensor device, a communication and data aggregation module, and a cloud-based AI-based inference engine that enables contextual, personalized, and continuous assessment of potential unwanted drug interactions. The invention utilizes a hybrid AI architecture from machine learning, natural language processing and probabilistic modeling for the analysis of drug data, physiological sensor data and patient-specific health records. The system is configured to proactively detect interactions, provide real-time warnings, and enable secure communication with healthcare providers.A novel component of the invention is the docking interface, a modular structure for diagnostics, data visualization, inductive charging of devices, and offline interaction with the system. The system supports the integration of blockchain for secure test paths and federated learning protocols to improve the accuracy of AI models while maintaining data protection. The invention thus provides a smart, adaptive and comprehensive solution for minimizing medication-related complications in the clinical, domestic and remote monitoring sector.The primary object of the present invention is to provide a comprehensive, intelligent system that provides real-time warnings of drug interactions. This is done by AI-based analyses of dynamic, patient-specific physiological data. This system overcomes the limitations of static, rule-based interaction testers by combining continuous biosensor monitoring with a robust artificial intelligence that personalizes and adaptively evaluates pharmacological, biochemical and context-related parameters. Another object of the invention is to ensure that detection of drug interactions is not limited to a particular treatment site such as a pharmacy or hospital, but can be integrated seamlessly into ambulatory and domestic environments through the use of a portable or implantable biosensor.Another object of the invention is the real-time recognition of pharmacokinetic and pharmacodynamic interactions by the analysis of temporal changes of physiological measurements such as heart rate variability, blood pressure, oxygen saturation, body temperature, liver enzyme activity and kidney markers, which are continuously detected via the wearable device. These biometric signals, in conjunction with AI-based inference, allow early prediction of undesirable interactions even before clinical symptoms occur. The system is also patient-centric and user-friendly designed, minimizes manual data input and provides functions such as automatic medication recognition based on visual identifiers, QR codes or NFC tags in medication packages.An important object of the invention is to close the gap between patient monitoring and clinical decision making by securely transmitting alerts and recommendations to healthcare providers, caregivers, and patients themselves in real time. This bidirectional information flow improves the joint decision making and ensures timely intervention in the case of potential interaction risks. The system also supports integration with electronic patient files (EHRs), E-recipe platforms and pharmacogenomic databases and thus allows an overall overview of the therapeutic environment and the genetic presentation of the patient.Another object of the invention is to provide a modular physical structure - the so-called docking interface - which serves as a diagnostic station, data input console and device loader. This structure extends the utility of the system by supporting additional diagnostic functions such as biochemical tests for creatinine, bilirubin or INR values. It also allows for a secure local interaction with AI recommendations and provides a graphical user interface for visualization of health trends and risk warnings. This multifunctional device centralises user interaction, supports offline operation and ensures reliable data exchange in areas of limited connectivity.Another object of the invention is to provide privacy, traceability, and transparency through the implementation of blockchain technology and federated learning protocols. These functions allow the system to continually improve its predictive models based on anonymous aggregated data while ensuring that sensitive personal health data remains safe and under the control of the single user. The invention is designed to conform to existing regulatory constraints such as HIPAA and DSGVO and is thus suitable for use in real health systems.In essence, the invention is intended to revolutionize the conventional approach to monitoring drug interactions. It introduces an intelligent, context sensitive and proactive system that not only identifies potential drug interactions, but also context-ifies based on physiological responses in real time, patient specific variables, and environmental factors. This intelligent combination of portable health technology, artificial intelligence, secure data management and human-centered design represents a significant advance in drug safety and is versatile in personal, clinical and remote health care.BRIEF DESCRIPTION OF THE FIGUREThese and other features, aspects and advantages of the present invention will become more fully understood by reading the following detailed description when taken in conjunction with the accompanying drawings, in which like numerals represent like parts throughout. The following applies here: FIG. 1 shows a block diagram of a system for real-time warnings of drug interactions using AI-based analyses.Those skilled in the art will also appreciate that the elements in the drawing are shown for simplicity and are not necessarily to scale. For example, the flowcharts illustrate the method using the key steps to improve understanding of aspects of the present disclosure. Also, as for the construction of the apparatus, individual or plural components of the apparatus may be represented by conventional symbols in the drawing. The drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art after the present description.DETAILED DESCRIPTION OF THE INVENTIONIn order to promote an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and will be described in an comprehensible manner. However, the scope of the invention is not limited thereby. Changes and further modifications of the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to a person skilled in the art.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the phrases "in one embodiment," "in another embodiment," and similar phrases in this specification may or may not refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also other steps not expressly listed or inherent in that process or method. Likewise, the phrase "comprises... for" one or more devices, subsystems, elements, structures, or components does not exclude, without further limitations, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by one of ordinary skill in the art. The systems, methods, and examples provided herein are for illustrative purposes only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.Referring now to FIG. 1, a block diagram of a system for real-time drug interaction warnings using AI-based analyses is shown. The system 100 includes: a wearable or implantable biosensor device (102) configured to continuously sense physiological parameters of a subject, including at least heart rate, blood pressure, blood oxygen saturation, skin temperature, and biomarkers associated with liver and kidney function; an in-device communication module (104) that securely transmits the physiological data to a cloud-based analysis engine via wireless protocols such as Bluetooth Low Energy (BLE), WLAN, or LTE-M; a cloud hosted artificial intelligence (AI) inference engine (106) configured to perform multimodal risk analysis of drug interactions, the engine comprising at least one convolutional neural network (CNN) for time-series analysis of biosignal patterns, a natural language processing (NLP) module (106a) for extracting drug information from structured and unstructured medical sources, and a Bayesian thinking module (106b) for modeling probabilistic risk interactions between drugs and patient-specific variables; a pharmaceutical input interface (108) for acquiring drug data by at least one of the following methods: manual input, optical character recognition (OCR) of packages or near field communication (NFC) scanning of embedded tags; a warning propagating module (110) configured to trigger multi-channel warnings including haptic signals about the wearable device, audio visual notifications about a paired smart device, and the secure transmission of interaction reports to particular medical professionals based on severity thresholds.In one embodiment, the biosensor device (102) also includes a miniaturized near infrared spectrometer (NIR) integrated on a flexible polymer substrate. The spectrometer can recognize hemoglobin variants and metabolic byproducts such as bilirubin and thus allows biochemical fluctuations to be correlated with the drug metabolism in real time.In one embodiment, the AI inference engine (106) is configured to operate using a federated learning protocol, where patient-specific model updates are executed locally on the device or on a secure edge node and merged into a global model via differential privacy updates, ensuring compliance with privacy regulations while improving population-wide prediction accuracy.In one embodiment, the Bayesian inference module (106b) receives as input pharmacogenomic data derived from user uploaded DNA reports or interoperable clinical databases and employs gene-drug interaction models that include CYP2C9, CYP2D6, CYP3A4, SLCO1B1, and VKORC1 allel profiles to dynamically adapt risk predictions for adverse drug effects.In one embodiment, the NLP module (106a) of the AI engine is trained using a corpus of at least 100,000 anonymous clinical notes, drug monographs, case reports on drug vigilance, and user generated electronic health record annotations and is configured to extract dosing instructions, routes of administration, contraindications, and known interaction patterns to enrich the interaction graph used by the Bayesian inference module.In one embodiment, the communication module (104) utilizes a dual path encryption strategy that includes session level encryption with AES-256 for biometric stream data and elliptic curve based asymmetric encryption for drug identity metadata, thereby ensuring tamper-proof and HIPAA compliant data transfer between heterogeneous devices and platforms.In one embodiment, the alert propagation module (110) includes a context classifier that determines the appropriate warning modality based on the user's activity state (e.g., sleeping, driving, in a clinical environment) derived from accelerometer data, GPS protocols, and the environment audio context, thereby minimizing alert fatigue and maximizing reaction efficiency to high-risk alerts.In one embodiment, a blockchain-based test protocol is maintained in parallel with the output of the AI engine so that each drug interaction alert, physiological trend analysis, and decision path of an AI model is time stamped, cryptographically looked up, and appended to a distributed ledger.The detailed description of the invention, supported by the above-mentioned system claims, describes a comprehensive and sophisticated system for real-time warning of drug interactions using AI-based analyses. The heart of the system is an advanced hybrid artificial intelligence that cooperates with a portable or implantable biosensor having multiple sensors and an optional, diagnosticable structural docking unit. The algorithm basis of the system is based on the integration of deep learning, probabilistic modelling and natural language understanding. Collectively, they interpret biometric, pharmacological and contextual data highly personalized and dynamic.The algorithm begins with real-time acquisition of physiological data that is continuously acquired by a biosensor worn or implanted by the patient. This instrument monitors a number of vital and biochemical parameters including heart rate variability, systolic and diastolic blood pressure, oxygen saturation (SpO 2), skin temperature, and biochemical indicators such as bilirubin, creatinine, and liver enzymes. The data is time stamped, encrypted, and securely transmitted to the cloud-based AI engine using a multi-layered encryption protocol. The system also utilizes federated learning to assist in decentralized model training. Thus, the computation directly on the device enables personalized predictions without making personal health data accessible to central servers.Once the biometric data is received, it is read into a convolutional neural network (CNN) specifically trained for processing time-series physiological signals. This neural network is designed to detect at an early stage subtilous disturbances in body systems which could indicate developing undesirable pharmacodynamic effects. If, for example, after the administration of a certain medicament, the system recognizes an unusual combination of an increased heart rate, an increased body temperature and a decreasing SpO 2- trend, the CNN identifies this as a temporal anomaly and forwards it to the probabilistic level for further risk context visualization.At the same time, the natural language processing module (NLP) of the AI engine analyzes medication data from different sources. These include direct medication input via the NFC scanner of the wearable or the camera-based OCR of the docking station, uploaded recipe files in text or PDF format and connected EHR or eRx platforms. The NLP model is trained on a large corpus of over 100,000 clinical notes, drug monographies, and case studies on drug vigilance. It extracts dosing instructions, routes of administration, time intervals and labelled contraindications. This text information is converted to structured metadata which is then used to create an interaction diagram between the newly introduced drug and the subject's existing pharmacological profile.A Bayesian inference engine receives input from both the CNN-derived biometric features and the NLP-extracted drug metadata. The machine uses probabilistic modeling to quantify the risk of interaction based on conditional dependencies between drug properties, known pharmacological mechanisms, and patient-specific variables such as age, kidney function, hepatic clearance capacity, and genetic markers. For example, if a patient has a CYP2D6 Poor Metabolismr genotype and receives a substrate drug whose clearance relies on CYP2D6, the Bayesian network models this pathway and assigns a high level of interaction when a second drug that is representatively CYP2D6 is administered simultaneously.The Bayes model is supplemented by a dynamic knowledge base that takes into account real data from market monitoring, side effects reported by users, and pharmacogenomic databases. The interaction scores generated by the engine are not static, but adapt to new findings over time. The assessment function simulates hypothetical scenarios by embedded pharmacokinetic modeling. Compartment analyses and Monte Carlo simulations predict how variations in dose time, enzyme saturation, or concurrent ingestion of food affect systemic drug concentrations and the resulting risk.Upon detection of a critical or urgent interaction, the system initiates a contextual alarm. The alarm modality is chosen based on the user's current context, as determined by environmental sensors and wearable integrated activity detection algorithms. For example, if sleep is detected, the system suppresses audible alarms and instead outputs haptic feedback and logs the event for tracking. During car drives, the system activates visual dashboard notifications or voice-based messages that are integrated over mobile platforms. At the same time, the same alarm is forwarded to the domestic doctor, the pharmacy or the emergency contact of the user in encrypted form, depending on the severity level.To ensure the integrity and traceability of each drug interaction score and AI inference, the system logs each decision event into a blockchain-based registry. Each data set contains the exact biosensor time series, the decision path of the algorithm, the drug metadata extracted by NLP, and the probabilistic results of the Bayesian inference module. Each event is cryptographically looked and time stamped to ensure a tamper-proof test path that is accessible only to medical professionals via secure access data.The algorithm framework of the invention illustrates a novel integration of biosignal based AI analysis, pharmacological thinking and real world context inference to enable safe and intelligent monitoring of drug interactions. By going beyond conventional rule-based systems and embedding advanced AI techniques directly in the pharmacovigilance process, this invention sets a new paradigm for personalized, real-time drug safety in different supply contexts.An integrated microcontroller processes sensor data and controls communication via BLE, WLAN or LTE-M. The device also has an encrypted memory for temporary data buffering and is operated by a battery which is inductively charged in the docked state.The AI inference engine is on a secure cloud platform and is based on a hybrid model. Natural language processing modules analyze and contextualize unstructured medical documents, recipe documents, and patient data. Convolutional neural networks (CNNs) time-series analyze biosensor data to detect drug-related physiological changes. Bayesian networks serve to probabilistically assess risk of drug, drug-gene and drug-state interactions.The AI model continuously learns using federated learning algorithms to ensure that improvements in prediction accuracy are passed to all users while the raw data remains decentralized.The interface is a multifunctional machine / structure, consisting of charging station, touchscreen and diagnostic module. It has an integrated imaging unit with image processing functions for identifying and validating medicines from pill form and label. The structure also supports sampling analyses by microfluidic cartridges inserted into an integrated slot to perform rapid biochemical measurements such as INR, bilirubin or creatinine values. The interface synchronizes with the wearable, uploads data to the cloud, and functions as a local processing node upon loss of connection.When a user takes a new medication, he scans his identification with the portable device or via the interface. The system logs this input, verifies the medication using NLP and image recognition, and initializes a contextual interaction scan. At the same time, the wearable device monitors physiological responses that may indicate early signs of undesirable interactions, such as sudden heart rate fluctuations, abnormal liver enzyme fluctuations, or elevated blood pressure.The AI engine correlates these parameters with known interaction databases, patient specific genetic markers (if available), and the medical history. Upon detection of a likely unwanted interaction, the system classifies its severity, urgency, and mechanism and sends multiple channel alerts: tactile haptics via the wearable device, visual and audible alerts on the user interface, and mobile or email-based alerts to the healthcare actors.All interactions are logged in a blockchain-secured registry. This ensures traceability for clinical, drug or insurance testing. Advanced models can also be integrated into E-recipe systems, hospital information systems (HIS) and patient portals and thus enable a seamless, bidirectional data flow.The present invention relates to the fields of biomedical monitoring systems, medical intelligence applications and digital pharmacovigilance. More particularly, the invention relates to a real time intelligent system for detecting and alerting of undesirable drug interactions. It uses wearable or implantable biosensors, AI-based analysis of time series-based physiological data and context-sensitive pharmacological risk models. The system is also connected to the Internet of Medical Things (IoMT), Clinical Decision Support Systems (CDSS), Mobile Health Technology, and Secure Health Information areas. It is intended to increase patient safety by providing proactive, personalized drug interaction alerts that respond to both real-time biometric feedback and to dynamically evolving therapy schemes.The drawings and the foregoing description show examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Moreover, the actions of a flow chart need not be performed in the order shown; nor do all actions necessarily need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is by no means limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages and solutions to problems have been described above with reference to specific embodiments. However, the advantages, merits, solutions to problems and any components that may result in an advantage, merit or solution being introduced or enhanced are not to be understood as critical, required or essential features or components of individual or all claims.REFERENCES100 System For Real-Time Warnings Prior to Interactions Of Drugs By AI-Assisted Analysis. 102 Portable or implantable biosensor device 104 Communication module 106 Cloud-hosted inference engine For Artificial Intelligence (AI) 106 a Natürlicher Natural Language Processing (NLP) module 106 b Modul Policy module 108 Pharmaceutical input interface 110 Alert propagation module

Claims

A system for real-time drug interaction awareness using AI-based analysis, comprising: a wearable or implantable biosensor device configured to continuously sense physiological parameters of a subject, including at least heart rate, blood pressure, blood oxygen saturation, skin temperature, and biomarkers associated with liver and kidney function; a communication module integrated with the device that can securely transmit the physiological data to a cloud-based analysis engine via wireless protocols such as Bluetooth Low Energy (BLE), Wi-Fi, or LTE-M; a cloud hosted artificial intelligence (AI) inference engine configured to perform multimodal risk analysis of drug interactions, the engine comprising at least one convolutional neural network (CNN) for time-series analysis of biosignal patterns, a natural language processing module (NLP) for extracting drug information from structured and unstructured medical sources, and a Bayesian reasoning module for modeling probabilistic risk interactions between drugs and patient-specific variables; a pharmaceutical input interface for acquiring drug data by at least one of the following methods: manual input, optical character recognition (OCR) of the package, or near field communication (NFC) scanning embedded tags; a warning distribution module configured to trigger multi-channel warnings including haptic signals about the wearable device, audio-visual notifications about a paired smart device, and the secure transmission of interaction reports to particular medical professionals based on severity thresholds.The system of claim 1, wherein the biosensor device further comprises a miniaturized near infrared spectrometer (NIR) integrated on a flexible polymer substrate, the spectrometer being capable of detecting hemoglobin variants and metabolic byproducts such as bilirubin, thereby allowing correlation of biochemical variations with drug metabolism in real time.The system of claim 1, wherein the AI inference engine is configured to operate using a federated learning protocol, wherein patient-specific model updates are executed locally on the device or on a secure edge node and merged into a global model via differential privacy updates, thereby ensuring compliance with privacy regulations while improving population-wide prediction accuracy.The system of claim 1, wherein the communication module uses a dual path encryption strategy that includes session level encryption for biometric stream data and elliptic curve based asymmetric encryption for metadata on drug identity, thereby ensuring tamper-proof and HIPAA compliant data transfer between heterogeneous devices and platforms.The system of claim 1, wherein the alert propagation module comprises a context classifier that determines the appropriate alert modality based on the user's state of activity derived from accelerometer data, GPS protocols, and the environmental audio context, thereby minimizing alert fatigue and maximizing reaction efficiency to high-risk alerts.The system of claim 1, wherein a blockchain-based test protocol is maintained in parallel with the output of the AI engine such that each drug interaction alert, physiological trend analysis, and decision path of an AI model is time stamped and cryptographically looked up.

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