Device for managing clinical tests and / or telemedicine medical equipment

Abstract

The invention relates to a method and device for managing clinical tests, the method comprising digitally inputting a dataset representing medical protocols associated with clinical tests relating to a disease; digitally associating each medical protocol with a clinical centre for carrying out medical tests relating to the corresponding medical protocol; digitally planning a plurality of medical tests associated with each medical protocol, such that each medical test has an associated time window for carrying out the respective medical test; and digitally associating a patient with a medical protocol and with users of the device, such that the users of the device are registered and notified to carry out one or more tests of the plurality of tests for the respective medical protocol within the time window.

Description

[0001] DEVICE AND METHOD FOR MANAGING CLINICAL TRIALS AND / OR TELEMEDICINE MEDICAL EQUIPMENT

[0002] TECHNICAL SECTOR

[0003] This disclosure pertains to the field of medical protocol management for clinical trials associated with specific medical conditions, including the organization of information related to these medical protocols, and / or the implementation of telemedicine using telemedicine equipment. More specifically, the disclosure refers to a device and method for managing and organizing information related to clinical trials conducted by medical entities, laboratories, and associated personnel, including patient status monitoring. Patient status monitoring may include patients awaiting treatment, during treatment, and / or after treatment, either directly or via telemedicine, enabling remote monitoring.

[0004] BACKGROUND OF THE INVENTION

[0005] Managing one or more clinical centers, that is, medical centers such as hospitals, laboratories, treatment centers, etc., to conduct medical trials involves a number of complexities, especially with regard to scheduling medical tests and monitoring patients undergoing such trials.

[0006] The limited availability of medical resources in clinical centers is well known, so suboptimal management is not only detrimental to the use of existing resources, but is also detrimental to the medical treatment of patients with the necessary guarantees, whether to carry out a clinical trial or to carry out telemedicine as a remote treatment, or to carry out both.

[0007] Thus, coordinating medical schedules and ensuring the availability of specialized equipment presents a constant challenge. The need to synchronize multiple calendars, considering medical specialties, the availability of medical equipment such as diagnostic and treatment equipment—whether located on-site or remotely (e.g., in a different hospital, at the patient's home, etc.)—and the urgency of each case, demands highly efficient management processes. Furthermore, the unpredictability of events such as emergencies or last-minute cancellations can cause delays and disrupt scheduling.

[0008] Part of medical resources also includes the human resources of clinical centers. These human resources, which in most cases must include qualified personnel specialized in clinical trials and / or specific medical tests, are essential not only to allow tests to be carried out on patients in a timely manner, but also to guarantee the quality of medical care. The unavailability of certain human resources, whether due to inadequate management or unforeseen absences, disrupts the functioning of clinical centers and leads to the misuse of all existing resources.

[0009] It is also essential that medical tests be tailored to the specific clinical trial, or to the patient's particular pathology and situation. Likewise, it must be considered that treatments and medical tests are contingent upon the patient's availability and / or the availability or performance of other medical tests that may have been or may need to be performed on the patient previously. For example, clinical trial protocols outline the tests to be performed in each phase of each trial. Thus, each clinical trial is based on a medical protocol that specifies, for example, the specific details of the medical tests to which patients will be subjected, the medical equipment used, the timelines for each test, the information to be collected, the various milestones during the trial, and so on.Given the number of tests that can be performed, managing, processing, and updating a large amount of clinical data at different times during trials is complex. Proper management, processing, and updating of clinical data allows for monitoring patient status and understanding the trial results for each individual patient, which is crucial for drawing conclusions about the overall clinical trial results. Genetic factors, general health status, and responses to treatment can vary considerably among patients, requiring continuous evaluation and, sometimes, adjustments to follow-up protocols. Therefore, from test results to patient medical records, information must be stored securely and be accessible to all professionals involved in patient care.Likewise, interoperability between different computer systems and / or between different clinical centers is essential to ensure fluid and effective communication.

[0010] One of the main challenges in clinical trials is ensuring the quality of the data obtained, which involves collecting data properly, maintaining patient follow-up information, and conducting regular audits. Another challenge is ensuring that patient or trial participant data is adequately protected, kept confidential (e.g., by anonymizing data when it will be disclosed to certain individuals or the general public), and that there is transparency and traceability, not only in the results but also in how the entire clinical trial was conducted, including any alterations in the treatment or scheduling of the patient that may not fully comply with the existing protocol.In clinical trials, it is essential to be able to compare the data from different participants in order to determine the efficacy of a given treatment, its potential side effects, and other factors. Therefore, it must be possible to draw conclusions from data from multiple participants without compromising confidentiality.

[0011] All these aspects are linked to ensuring patient safety and knowing their condition during all phases, and this must be done in compliance with health regulations that impose strict requirements in terms of risk management, quality and data protection, including traceability requirements for the entire process to which a patient has been subjected, for example but without limitation, the General Data Protection Regulation or Part 11 of Title 21 of the United States Code of Federal Regulations.

[0012] Monitoring and managing patient compliance with medical treatments in clinical trials is carried out using various devices, requiring manual coordination of completed and pending medical tests. Typically, independent devices or software adapted to specific medical tests are used, with monitoring via email alerts or handwritten entries in logbooks to ensure adherence to the medical protocol. This type of monitoring and management, largely manual in clinical trials, is prone to human error, which can result in a test not being performed on a patient or being carried out outside the timeframe established in the protocol.This can lead to a consequent alteration in the results of the clinical trial in question, and even a potential cascade effect in clinical centers: delays in other possible medical tests, preventing existing resources from being used for another patient. It also complicates the management of medical staff across different departments or clinical centers.

[0013] When remote monitoring, typical of telemedicine, is implemented in addition to or as an alternative to other methods, the complexity arises of accurately determining the results of a patient's test. In this regard, it is crucial to know precisely the measurements and / or applications performed by medical teams on patients in order to determine the medical protocol to which each patient has been subjected and their current health status. It is well known that many pathologies have symptoms that can only be detected through rigorous analysis of existing patient data. Therefore, having the necessary equipment available remotely presents a challenge in obtaining such data with the appropriate level of detail.

[0014] It can be seen, therefore, that the management of clinical centers and the monitoring of patients when clinical trials are carried out and / or remote equipment is used are technically complex processes, these processes being integrated by the computer devices that carry out all the digital management of the multiple resources.

[0015] Furthermore, another challenge facing the management of clinical trials and / or telemedicine medical teams is that, despite advanced information and coordination systems, their operational approach remains essentially reactive. This means that crucial problems, such as delays, protocol deviations, or resource overload, are only detected after they have already occurred. This retrospective approach severely affects the scientific validity of ongoing studies, impacts committed timelines, and significantly increases contingency costs. Therefore, it is essential to have state-of-the-art methods and devices for managing and organizing clinical trials and remote teams that allow for the centralization of all information related to the corresponding patient and their associated medical protocol.Furthermore, it would be preferable for such methods and devices to be capable of automating the monitoring and control of the patient, the medical protocol to which they are subjected, and / or the different stages of the clinical trials. It is also necessary that these methods and devices be able to interpret all trial-related information in a unified manner and be able to identify risk patterns and anticipate operational or clinical deviations before they materialize.

[0016] EXPLANATION OF THE INVENTION

[0017] The first object of this disclosure relates to a device for managing clinical trials and / or telemedicine medical equipment, and the associated medical protocols. The device comprises a means for digitally inputting a set of data indicative of one or more medical protocols associated with one or more clinical trials related to a specific pathology. The pathology for which the clinical trial is designed can be of any kind, such as, for example, pathologies associated with the circulatory system, cardiovascular diseases, musculoskeletal disorders, diabetes, respiratory diseases, etc.

[0018] Furthermore, the device includes means for digitally associating each of the one or more medical protocols with at least one clinical center for the performance of at least one medical test related to the corresponding medical protocol. The device also includes means for digitally scheduling multiple medical tests associated with each of the one or more medical protocols, such that each medical test has an associated time window for its performance. The time window for performing the medical tests refers to the time period, which may relate to various milestones connected with the medical protocol, such as the start of the clinical trial itself or the completion of a previous medical test, among others, within which the established medical test must be performed.

[0019] The device also includes a means to digitally associate a patient with a medical protocol and one or more device users, such that these users are registered and notified to perform one or more of the multiple medical tests within the respective medical protocol, according to the corresponding time window. Device users may be medical personnel authorized to monitor and follow up with the patient in question, or administrative staff of the clinical center responsible for monitoring and following up with the medical protocol associated with the clinical trial. Clinical centers may include hospitals, outpatient clinics, medical laboratories, research centers, or any other medical facility qualified to perform one or more procedures within a medical protocol associated with the clinical trial.

[0020] Through various partnerships, it is possible to coordinate clinical trials for specific pathologies, in turn with one or more medical protocols (involving multiple medical tests) and one or more clinical centers, the timeframes for conducting medical tests, and the medical personnel. This establishes a sequence for each patient that defines their pathology, their trial, which medical tests should be performed and at which center or centers, at what times, and by which members of the medical staff. The medical personnel, in turn, are informed of the patients they must treat with medical tests, when they should treat them, and how, and are duly notified to carry out these tests.

[0021] Each clinical trial has an associated group of patients, and it is through the existing hierarchies between clinical trials, patients, and medical tests that an overall clinical trial result can be determined from the data generated for that trial, even as the number of patients increases. The data available for consultation by each user can be adjusted to digitally associate the patient with a medical protocol and one or more users, who in some implementations are configured for this purpose. In this way, only certain data is exposed to specific users and / or the corresponding data anonymization is carried out, if necessary.For example, when the device generates reports, the amount of information provided varies depending on the device's configuration, which is determined by the type of report being generated. Data exposure can therefore be configured per user and / or per task (e.g., report to generate, notification to send, milestone to record on the device, etc.), allowing granular control over the data available to the device. In this regard, when certain data should not be exposed in a specific query, an anonymization routine can be executed to prevent access to that data. This anonymization routine can delete or encrypt the necessary data using state-of-the-art techniques. All of this results in data management that complies with the required data protection standards.

[0022] In some embodiments, the dataset indicative of one or more medical protocols associated with one or more clinical trials relating to a pathology comprises at least one of the following: a clinical trial identification code, a clinical trial patient identification code, a clinical trial phase, and information relating to the clinical trial, including the medical protocol associated with said clinical trial. As described herein, the medical protocol refers to the detailed plan of the clinical trial or experiment.

[0023] The medical protocol at least establishes what will be done in the study and how. Optionally, the protocol also establishes one or more of the following: why one or more procedures will be performed, how many patients are participating or will participate, who is eligible to participate, which study drugs or other interventions will be used, what tests will be performed on patients, how often and when these tests will be carried out, and what information will be collected. The medical protocol may be a medical protocol designed by one of the clinical sites that will conduct the clinical trial or a medical protocol provided to the clinical site by the clinical trial sponsor, for example, a pharmaceutical company.

[0024] In some implementations, the means for digitally associating each of the one or more medical protocols with at least one clinical site are also configured to associate at least one group of authorized medical tests with each medical protocol. That is, since the medical protocol defines which tests will be performed on patients, how frequently, and when, this information will be associated with each of the clinical sites participating in the clinical trial. This prevents a clinical site from performing tests on a patient that are not included in, and therefore not authorized by, the medical protocol associated with the clinical trial in question.In some implementations, the means for digitally planning the multiple medical tests associated with each of one or more medical protocols are configured to determine a series of medical tests for each patient based on their condition and the medical protocol associated with the corresponding clinical trial. By associating each patient with the appropriate medical tests, the system is able to schedule only the tests relevant to them. This allows the system to avoid performing unnecessary tests on patients, thus preventing the resulting physical discomfort, additional expenses, and the unnecessary use of limited resources.

[0025] Additionally, the planning process can also take into account the availability of medical resources at the relevant clinical center. These resources may include: one or more medical facilities, one or more pieces of medical equipment, and one or more individuals at the clinical center (preferably one or more users of the device) suitable for performing the medical tests within the series of medical tests.

[0026] In some implementations, the means for digitally scheduling the multiple medical tests associated with each of the one or more medical protocols are configured to link a time slot to each medical test for a patient and / or a clinical staff member associated with one or more medical tests from the multiple medical tests (e.g., one or more device users). This allows healthcare personnel, by accessing the time slots for a single patient, to schedule the patient's medical tests so that they do not overlap and / or are performed in the appropriate order and with the appropriate intervals in each case. Additionally, or alternatively, medical tests can be scheduled according to the healthcare personnel's time slots to ensure availability within the respective time window.Each test schedule is recorded on the device, providing traceability in how the patient is treated; and, likewise, in case of test rescheduling, each rescheduling is treated as an event caused by a specific user, which is also recorded on the device for traceability purposes.

[0027] In some implementations, the digital scheduling tools for the multiple medical tests associated with each of the one or more medical protocols are configured to generate alerts for each test within its timeframe. Specifically, the scheduling tools are configured to send alerts—such as emails to inboxes, text messages (e.g., SMS, messaging app messages, etc.) to mobile devices (e.g., mobile phones, tablets, laptops, etc.)—to both the patient undergoing the test and the healthcare or administrative staff at the clinical center responsible for organizing the tests.

[0028] In some embodiments, the device includes storage for the results of medical tests performed on patients in clinical trials. These test results may be from tests established by the trial's medical protocol and / or other medical tests performed on the patients. For example, if a patient has test results from clinical sites other than those listed in the medical protocol, these results can be entered into the device (e.g., via a device input / output system, a digital download from a server, etc.) and associated with the corresponding patient. This information can be stored locally on the clinical site's own memory or servers, or on external, remote servers.

[0029] In some embodiments, the device comprises digital communication media with one or more medical devices. The communication media may carry out direct communication, i.e., between the device and the medical device in question, or indirect communication, i.e., between the device and the medical device in question there is one or more communication networks such as, for example, the Internet, a local area network, a personal area network, etc. The communication media may include one or more devices to enable wired and / or wireless communication (e.g., a wireless network card, a wired network card, one or more Ethernet cables, one or more routers, etc.).

[0030] Through digital communication channels, the device communicates with one or more medical teams to conduct telemedicine. The device can then obtain data from the medical team and, optionally, configure the team, for example, by adjusting the dosage of a medication administered by the medical team. All data obtained can be directly associated with the patient via a patient identifier.

[0031] These digital communication channels also allow one or more remote medical teams at a clinical center, which may not even be participating in a clinical trial, to obtain patient data. The data obtained can be processed by the device. For example, the device can generate a report or a dataset resulting from the processing of the obtained data, either autonomously and / or with the interaction of one or more device users (e.g., a specialist, a physician, etc.), and transmit any data processing results to the originating clinical center via digital communication channels. Once the clinical center accepts the data transmission, it can record the data received from the device to gain a more detailed understanding of the patient's condition, for example. Thus, the device enables telemedicine to be conducted outside the context of a specific clinical trial.

[0032] In some embodiments, the storage media are configured to store at least one of: patient images and patient medical reports, associated with a patient identifier and with an identifier of the corresponding medical protocol and / or clinical trial.

[0033] In some embodiments, the device includes means for displaying images associated with tests performed on the patient. These means may be an image viewer or player, or any other application that allows the display of digital images stored in local or remote memory.

[0034] In some implementations, the storage media are configured to store a history of medical tests performed and / or medical reports issued for each patient. This history allows for better monitoring and control of patients who have participated in one or more clinical trials.

[0035] The device described here centralizes and automates the management, coordination, and organization of clinical trials, as well as their associated medical protocols, minimizing human error in these processes. This ensures that clinical trial results are consistent and reliable. Furthermore, the device allows for more effective, and even optimal or near-optimal, use of existing medical resources by reducing or eliminating the need to schedule medical tests when one or more medical resources are unavailable, or when there are any issues (on the part of the patient and / or the clinical center) with little time (e.g., a week, a day, half a day, etc.) to perform a medical test, thus reducing the risk of treating the patient incorrectly.Similarly, the device reduces the paperwork and bureaucracy associated with these types of processes by centralizing all the information on a single device, which also centralizes all the information related to the clinical trial results. Thanks to this device, the management of all this information is facilitated, simplifying the monitoring of patients' health status and the tracking of the phase the patient is in within the clinical trial.

[0036] In some embodiments, the device comprises one or more processors and one or more memories. In some embodiments, the various means described above are implemented by means of the one or more processors and the one or more memories. For example, the one or more memories may comprise a computer program with instructions suitable for execution by the one or more processors to carry out one or more of the embodiments described above.The instructions may include, for example: digitally associating each of one or more medical protocols with at least one clinical center for the performance of at least one medical test related to the corresponding medical protocol; digitally planning a plurality of medical tests associated with each of one or more medical protocols in such a way that each medical test has an associated time window for the performance of the respective medical test; and digitally associating a patient with a medical protocol and one or more device users in such a way that the one or more device users are registered and notified to perform one or more tests from the plurality of tests of the respective medical protocol according to the respective time window.

[0037] In some embodiments, the device comprises one or more databases for storing the data generated, processed, and queried by the various means described above. In some embodiments, the one or more databases are non-relational, e.g., NoSQL; in other embodiments, the one or more databases are relational, e.g., MySQL.

[0038] In some embodiments, the device consists of one or more computers and / or one or more servers, with the one or more computers and / or the one or more servers configured to communicate and exchange digital data.

[0039] In some implementations, the device is part of a digital cloud configured to provide a software service, i.e., software as a service (SaaS), for the management, organization, and monitoring of clinical trials.

[0040] In some embodiments, the device comprises predictive processing means, preferably AI-based processing means. These means comprise a processing unit that executes at least one predictive model, preferably AI-based, and are configured to perform predictive analyses on data generated by the means for digitally inputting a dataset indicative of one or more medical protocols associated with one or more clinical trials related to a pathology, the means for digitally associating each of the one or more medical protocols with at least one clinical center, the means for digitally scheduling a plurality of medical tests associated with each of the one or more medical protocols, and the means for digitally associating a patient with a medical protocol and one or more users of the device.These analyses yield metrics that assess the degree of compliance with the clinical trial's medical protocol. The operational status of the clinical trial is determined based on these compliance metrics and operational data (which may include traceability records, the status of scheduled visits, time elapsed relative to time windows, and the performance of clinical sites). Furthermore, potential clinical or operational risks or deviations are anticipated / predicted based on the clinical trial's operational status, compliance metrics, and operational data before they occur, generating alerts. Integrating these predictive processing tools into the device transforms it from a mere information management system into an intelligent clinical trial management system.This provides greater scientific and regulatory certainty by ensuring protocol integrity and anticipating non-compliance that would compromise the trial's validity. Furthermore, it enhances operational efficiency by proactively identifying bottlenecks, centers with recurring issues, and resource overload before they cause delays. It also enables better patient monitoring by detecting behaviors that foreshadow non-adherence, allowing for early intervention to reduce the risk of dropout. Taken together, these predictive processing capabilities give the device superior control, with real-time monitoring, predictive analytics, and proactive action capabilities. For example, the predictive processing module can store and run predictive models such as regression models, random forest models, time series models, and so on.

[0041] In some embodiments, the predictive processing tools, preferably AI-based, are further configured to perform predictive analyses not only on the operational input data described in the preceding paragraph, but also on data previously stored on the storage media. This previously stored data comprises the dataset indicative of one or more medical protocols associated with clinical trials for a specific pathology, data relating to the associations between these protocols and clinical centers, the detailed planning of the medical trials, and the association of patients with the protocols and device users.The use of this structured and consolidated information by predictive processing tools significantly optimizes control capabilities, enabling more robust real-time monitoring, more accurate predictive analysis, and greater effectiveness in the device's proactive action capacity.

[0042] A second subject of this disclosure relates to a computer-implemented method for managing one or more clinical trials.This method comprises the steps of: digitally entering a data set indicative of one or more medical protocols associated with one or more clinical trials relating to a pathology; digitally associating each of the one or more medical protocols with at least one clinical center for the performance of at least one medical test relating to the corresponding medical protocol; digitally planning a plurality of medical tests associated with each of the one or more medical protocols in such a way that each medical test has an associated time window for the performance of the respective medical test; and digitally associating a patient with a medical protocol and one or more users of the device in such a way that the one or more users of the device are registered and notified to perform one or more tests from the plurality of tests of the respective medical protocol according to the respective time window.

[0043] In some embodiments, the data set indicative of one or more medical protocols associated with one or more clinical trials relating to a pathology may comprise at least one of the following: a clinical trial identification code, a clinical trial patient identification code, a clinical trial phase, and information relating to the clinical trial, including the medical protocol associated with the clinical trial.

[0044] In some implementations, the method involves associating at least one group of authorized medical tests for each medical protocol.

[0045] In some implementations, the method involves determining a series of medical tests for each patient based on the patient's pathology and the clinical trial.

[0046] In some implementations, the method involves generating alerts associated with the time window of each of the tests.

[0047] In some implementations, the method involves associating a separate time schedule with each medical test to be performed on the same patient.

[0048] In some embodiments, the method includes storing at least one patient image and one patient medical report, associated with a patient identifier and a corresponding clinical trial identifier. Preferably, a history of medical tests performed on each patient can also be stored.

[0049] In some embodiments, the method may also include visualizing, using visualization means, images associated with medical tests performed on the patient. In some embodiments, the method includes performing predictive analysis, preferably an Artificial Intelligence (AI)-based predictive analysis, on the data generated by the means to digitally input a dataset indicative of one or more medical protocols associated with one or more clinical trials related to a pathology, the means to digitally associate each of the one or more medical protocols with at least one clinical center, the means to digitally schedule a plurality of medical tests associated with each of the one or more medical protocols, and the means to digitally associate a patient with a medical protocol and one or more users of the device.This yields metrics that allow for the predictive evaluation of adherence to the clinical trial's medical protocol, the determination of the trial's operational status, and the anticipation / prediction of potential risks or deviations before they occur. For example, an AI-based processing module could be used to implement predictive algorithms such as Logistic Regression and Random Forest models, among others, to predict operational risks (such as patient dropout or missed appointments), and Time Series models to anticipate delays in the trial schedule. Convolutional Neural Networks (CNNs), among other technologies, could be used for processing complex clinical data.

[0050] In some embodiments, the method comprises performing predictive analysis on data stored on storage media, which includes: the data set indicative of one or more medical protocols associated with one or more clinical trials relating to a specific pathology, data relating to associations between medical protocols and clinical centers, data relating to the planning of medical tests and the association of patients to medical protocols and device users.

[0051] In some embodiments, the method comprises: processing, using an AI-based medical image processing module, medical images obtained from medical tests performed on the patient during the clinical trial; performing, using the AI-based processing and diagnostic module, an automated clinical diagnosis of the patient based on the processed medical images; and associating the resulting clinical diagnosis with the patient. The use of this medical image processing module not only provides an objective clinical assessment but also automates the reporting and standardization processes for image interpretation, thereby improving the integrity and speed of the clinical trial's administrative and supervisory workflows.Medical images (such as computed tomography images, magnetic resonance imaging or imCT, MRI or PET) obtained from medical tests performed on patients during the course of the clinical trial could be processed by deep learning models such as Convolutional Neural Networks, CNNs algorithms based on fast detection and Generative Adversarial Networks, among many others.

[0052] The automation of medical image analysis to obtain objective clinical metrics represents a significant advancement. The introduction of this automated clinical interpretation allows for the integration of an additional layer of automation directly into the management of medical protocols for clinical trials associated with specific pathologies. By providing a rapid and standardized AI-driven clinical assessment for all centers and patients, operational decisions such as case prioritization, activation of specific follow-ups, and verification of response milestones (such as Response Evaluation Criteria in Solid Tumors, RECIST) can be automated, improving efficiency and ensuring adherence to protocol procedures without immediate manual intervention.

[0053] In some embodiments, the processing of the patient's medical images by means of an Artificial Intelligence-based medical image processing module comprises at least one of the following sub-steps: a) automatically measuring lesion diameters according to oncological standards, preferably according to RECIST or iRECIST (immune-RECIST) standards; b) segmenting and calculating a volumetric, for example, tumor volume, associated with the lesions; and / or c) obtaining advanced functional parameters, such as SUVmax or Total Lesion Glycolysis (TLG), from medical images, for example, positron emission tomography images, of the lesions.

[0054] In some embodiments, the method comprises validating, through an Artificial Intelligence-based medical image processing module, the conformity of the medical image acquisition with the medical trial protocol, where the validation comprises: automatically detecting deviations in critical technical parameters of the medical image; and verifying consistency of image acquisition between multiple centers, ensuring the integrity and technical quality of the clinical data obtained.

[0055] In some embodiments, the method comprises: predicting, using an AI-based medical image processing module, disease progression or a patient's response to a treatment being administered, based on a radiomic analysis of medical images, for example, PET images; and generating a Clinical Risk Indicator for the patient from the predictions generated; anticipating possible clinical or operational risks or deviations based on the operational status of the clinical trial, compliance metrics, operational data, and the Clinical Risk Indicator before they occur.

[0056] In some implementations, the stage of anticipating possible risks of clinical or operational deviations involves performing an automatic prioritization of cases by combining the derived Clinical Risk Indicator with a set of operational risks, which include at least delays or non-compliance with the clinical trial, to generate alerts about potential deviations from the medical protocol associated with the clinical trial.

[0057] A third object of the present disclosure is a computer program comprising instructions that, when the program is executed by a processor, cause the processor to carry out the steps of the method described above.

[0058] A fourth object of the present disclosure is a computer-readable storage medium comprising instructions that, when executed by a processor, cause the processor to carry out the steps of the method described above.

[0059] BRIEF DESCRIPTION OF THE DRAWINGS To complement the description being made and in order to help a better understanding of the characteristics of the invention, a set of drawings is included as an integral part of said description, in which, for illustrative and non-limiting purposes, the following has been represented:

[0060] Figure 1.- Shows a device for clinical trial management, according to a disclosure realization.

[0061] Figure 2.- Shows a schematic view of possible partnerships for clinical trial management, according to a disclosure realization.

[0062] Figure 3.- Shows a schematic view of a graphical interface where at least part of the information managed by the device for the management, organization and monitoring of clinical trials is visible, according to a realization of the disclosure.

[0063] Figure 4.- Shows a flowchart of the method for managing clinical trials, according to a disclosure.

[0064] DETAILED DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 shows a device 100 for the management, organization and monitoring of clinical trials, and of the medical protocols associated with said clinical trials, according to a disclosed embodiment of the invention.

[0066] The device 100 comprises means 101 for digitally inputting a set of data 108 indicative of one or more medical protocols associated with one or more clinical trials related to a pathology. The data indicative of the medical protocols may include an identifier for the corresponding trial, an identifier for each patient 106 in the trial, the trial title, a medical phase of the trial, an identifier of the trial sponsor (e.g., the name of the pharmaceutical company organizing and / or funding the study), and a series of medical protocol files associated with the trial (which may include approvals, procurement manuals, the medical protocol itself, etc.), among other information. This data may be sent to the means 101 for digital input by the study sponsor and / or by the clinical centers where the study is being conducted.The patient identifier 106 can be used across different clinical centers, whether or not they are participating in the clinical trial in question, to directly link the results of medical tests to a specific patient 106. For example, when a patient 106 is participating in two clinical trials simultaneously, the medical tests performed on patient 106 for the first clinical trial at certain clinical centers can also be linked to patient 106 for their consultation regarding the second clinical trial, regardless of whether the second clinical trial is conducted at the same or different clinical centers.

[0067] Furthermore, the device 100 comprises means 102 for digitally associating each of one or more medical protocols with at least one clinical center for the performance of at least one medical test related to the corresponding medical protocol. Clinical centers may be hospitals, outpatient clinics, research centers, laboratories, etc. These means 102 for digital association are also configured to input an identifier associated with the different clinical centers linked to each medical protocol, as well as contact information for those clinical centers and the trial sponsor(s).

[0068] As shown in Figure 1, the device 100 also has means 103 for digitally planning a plurality of medical tests associated with each of the one or more medical protocols, such that each medical test has an associated time window for its performance. These means 103 for digital planning allow time windows to be assigned for each of the tests to which the patient 106 will be subjected in the trial.

[0069] The device 100 also comprises means 104 for digitally associating a patient 106 with a medical protocol and one or more users (e.g., medical personnel 107) of the device 100, such that the one or more users of the device 100 are registered and notified to perform one or more tests from the plurality of tests of the respective medical protocol according to the respective time window. That is, the device 100 is capable of sending notifications, such as alerts, to both the patient 106 and the registered clinical staff (medical personnel 107 or administrative personnel) responsible for performing the medical test, or for managing the medical resource in question, e.g., those responsible for the facilities or laboratories required to carry out the medical test.This allows for automated and controlled digital management of the medical tests required by the medical protocol associated with the clinical trial in question. The medical staff member 107 in charge of managing the trial to be applied to patient 106 only needs to associate patient 106 with the relevant clinical trial and add any other data that may be relevant to the medical procedure.

[0070] All information relating to dataset 108, indicative of one or more medical protocols associated with one or more clinical trials relating to a pathology, the associations between medical protocols and clinical centers, the planning of medical tests, and the association of patients 106 with medical protocols and users of device 100, can be stored on storage media 105 associated with device 100. These storage media 105 can be, for example, one or more databases, one or more local or remote servers, etc. Furthermore, although Figure 1 shows storage media 105 for the information external to device 100, these storage media 105 could also be an integral part of device 100 itself.

[0071] Device 100 can also incorporate predictive processing means 113 configured to assess protocol compliance, determine the actual status of the clinical trial, and anticipate operational or clinical risks. These means receive data / information from means 101-105 and are capable of intelligently interpreting it in a unified manner, identifying relevant patterns, and anticipating potential deviations before they materialize. These predictive processing means 113 are defined as the functional combination of hardware and software (such as CPUs, GPUs, and memory modules) configured to execute advanced Artificial Intelligence (AI) and Machine Learning algorithms.These algorithms may include, but are not limited to, Convolutional Neural Networks (CNNs) for image processing, Gradient Boosting models and Support Vector Machines (SVMs) for operational risk classification, or Radiomics models for clinical prognosis. The purpose of these methods is to apply such predictive processing to trial data to transform historical and current information into anticipatory metrics, enabling proactive assessment of protocol compliance and prediction of operational or clinical deviations. The functionalities of these processing methods are described in greater detail in relation to Figure 4.

[0072] The device 100 can also integrate AI-based image processing means 114 specifically configured for the automated analysis and interpretation of medical images. These means 114 receive image data (such as CT, MRI, or PET) generated during medical tests and are capable of intelligently processing it to quantify objective clinical metrics, validate acquisition quality, and diagnose potential patient injuries. Such image processing means 114 are defined as the functional combination of hardware and software (such as CPUs, GPUs, and memory modules dedicated to visual tasks) configured to execute advanced Artificial Intelligence (AI) and Deep Learning algorithms.These algorithms may include, but are not limited to, Convolutional Neural Networks (CNNs) for tumor segmentation and lesion quantification (RECIST, volumetry), and Radiomics models for clinical prognosis (prediction of progression or response). The purpose of these methods is to transform medical images into standardized clinical metrics that inform and optimize the administrative and management processes of the trial protocol. The functionalities of these processing methods are described in greater detail in relation to Figure 3.

[0073] Figure 2 shows a schematic view of possible partnerships for clinical trial management 110, according to a disclosure realization.

[0074] Each clinical trial 110 has approximately 106 associated patients. A single patient 106 may participate in multiple clinical trials 110 conducted at different times or even with some overlap. In this regard, a completed clinical trial 110 may remain recorded on a device indefinitely, according to disclosure requirements, or for time periods as established by data regulations.

[0075] Clinical trials 110 are associated with a medical protocol 111. Each medical protocol 111 is associated with a specific battery of medical tests, which may be related to one another, for example, temporally. In such cases, a hierarchy is established in the medical protocol regarding how the medical tests should be performed on each patient. This, in turn, establishes time windows for each medical test, which the device can process automatically in order to schedule part or all of the battery of medical tests to be performed on each new patient 106 in the clinical trial 110.

[0076] A medical protocol 111 or the medical tests thereof are associated with one or more clinical centers 112, which in turn are associated with the clinical trial 110, so that both clinical centers 112 and medical protocols 111 are linked to the clinical trial 110. Likewise, clinical centers 112 may be linked to a clinical trial 110 without the center or centers being going to perform medical tests, for example, a sponsor of the clinical trial 110.

[0077] There is also medical personnel 107 associated with clinical centers 112 and / or the medical tests of a specific medical protocol 111, but this medical personnel 107 is also associated with the clinical trial 110 due to the relationships established between the different layers described (i.e., clinical trials 110, medical protocols 111, and clinical centers 112). Each member of the medical personnel 107 may be a user of a device according to the disclosure, and have one or more of the following data registered, such as: a temporary schedule 113, a set of permissions 114 that establishes how they can use the device, contact information to notify patients of the need to perform medical tests 106, etc.Different users may therefore not have the necessary privileges to consult certain data about patients 106, which can be established independently for each user per clinical trial 110 and / or medical protocol 111 and / or clinical center 112. It is also possible to define user groups (e.g., administrative staff, specialist medical staff of a specific area, etc.) to adjust privileges globally.

[0078] Figure 2 illustrates, as an example, the relationships between clinical trials 110, medical protocols 111, clinical centers 112, and medical personnel 107 using arrows. Although, for the sake of clarity, only these relationships with respect to clinical trials 110 are shown, it should be understood that relationships are established at the patient 106 level within each clinical trial 110. Each patient 106 undergoes medical tests and is assigned a corresponding timeline, and the various data for that patient 106, such as the results of the medical tests, are associated with that specific patient 106. Each respective dataset is recorded on the device, allowing for its consultation and traceability by authorized users.

[0079] The various relationships can be stored, for example, in one or more databases, which can be relational or non-relational. Data relating to patients 106, medical personnel 107, clinical trials 110, medical protocols 111, clinical centers 112, medical tests, temporary schedules 113, among others, can be stored as key-value data models, columnar data models, table data models with rows and columns, etc.

[0080] Figure 3 shows a schematic view of a graphical interface 200 where at least some of the information managed by the device for the management, organization and monitoring of clinical trials is shown, according to an implementation of the disclosure.

[0081] As shown in Figure 3, the graphical interface 200 displays a first module 201 containing general trial information, comprising the “Data” submodule 202 and the “Protocol Files” submodule 203. The “Data” submodule 202 stores an identification code 204 for each patient in the trial, a trial identifier 205, the trial title 206, the medical phase 207 in which the trial is located (e.g., phases II and V), and a sponsor identifier 208 for the trial (e.g., the name of the pharmaceutical company that organizes and / or finances the study). The “Protocol Files” submodule 203 stores a series of medical protocol files associated with the trial, which may include approvals, procurement manuals, the medical protocol itself, and other information. In addition, this first module 201 may include notes from the medical staff regarding the protocol and / or the trial.

[0082] The graphical interface 200 also includes a second module 209, which in turn comprises the submodule “Reference Centers” 210 and the submodule “Clinical Center Files” 211. The submodule “Reference Centers” 210 stores the clinical center identifiers 212 (hospitals, outpatient clinics, laboratories, research centers) participating in the trial. These clinical center identifiers 212 may be associated with an imaging center identifier, which identifies the clinical center where medical tests involving the acquisition of medical images are performed, such as computed tomography (CT) scans, positron emission tomography (PET) scans, X-ray images, and the like. The submodule “Reference Centers” 210 may also store contact information.

[0083] 213 of the clinical centers and / or the staff of the aforementioned centers, and contact details

[0084] 214 of the sponsor (for example, the pharmaceutical company sponsoring the trial). This contact information 213, 214 may include names of responsible parties, email addresses, telephone numbers, billing information, etc. The “Reference Centers” submodule 210 may also store a list of the imaging tests 215 that have been predefined for the medical protocol in question.

[0085] The graphical interface 200 also includes a third module 216, which in turn comprises the submodule “Pathologies” 217 and the submodule “Planning” 218. The submodule “Pathologies” 217 stores information relating to the pathologies 219 that are the subject of the clinical trial, the medical criteria 220 applied in the clinical trial, and the histologies 221 required to carry out the clinical trial. The submodule “Pathologies” 217 also allows the selection of the pathology and / or the medical criteria and / or the necessary histologies, and the device automatically generates a list of the tests required for the patient in question based on the selection made.

[0086] The "Planning" submodule 218 comprises the planning tools that automatically generate the schedule of medical tests to be performed on the patient and the deadlines for these tests. It can also display the timeframe in which authorized personnel must request the tests from each associated clinical center. For example, if a specific medical test is scheduled for February 10th, the "Planning" submodule 217 can show, in addition to the test date, that the test must be requested from a specific clinical center 3 to 5 days in advance. This "Planning" submodule 217 also generates the test reminder notifications that are sent to the patient, medical personnel, and the clinical center involved.

[0087] Notwithstanding what is illustrated in Figure 3, it should be understood that other embodiments of the disclosure may display more or less data in a graphical interface, with modules and submodules like those illustrated or additional ones, and / or instead of some of those illustrated, with a similar or different arrangement. Figure 4 shows a flowchart of Method 300 for clinical trial management, according to one embodiment of the disclosure. This method comprises the following steps:

[0088] 301 Digitally input a data set indicative of one or more medical protocols associated with one or more clinical trials relating to a pathology; 302 Digitally associate each of the one or more medical protocols with at least one clinical center for the performance of at least one medical test relating to the corresponding medical protocol; 303 Digitally plan a plurality of medical tests associated with each of the one or more medical protocols in such a way that each medical test has an associated time window for the performance of the respective medical test; and 304 Digitally associate a patient to a medical protocol and one or more users of the device in such a way that the one or more users of the device are registered and notified to perform one or more tests from the plurality of tests of the respective medical protocol in accordance with the respective time window.

[0089] Method 300 also comprises performing predictive analysis, using predictive processing means 113, on data generated by the means to digitally input a dataset indicative of one or more medical protocols associated with one or more clinical trials relating to a pathology, the means to digitally associate each of the one or more medical protocols with at least one clinical center, the means to digitally plan a plurality of medical tests associated with each of the one or more medical protocols, and the means to digitally associate a patient with a medical protocol and one or more users of the device. Through this predictive processing methodology, the system not only manages information but actively derives key anticipatory metrics.This allows for dynamic evaluation of future compliance with the medical protocol, determination in advance of the likely operational status of the trial, and proactive anticipation of potential risks or clinical or operational deviations before they materialize.

[0090] These predictive analyses, which we carry out using predictive processing equipment 113, digitally analyze, jointly or independently, the following data / information, for the subsequent generation of estimates / predictions:

[0091] • the time windows of planned medical tests; • the scheduling, rescheduling, or cancellation events recorded for each medical test;

[0092] • the actual or expected availability of participating clinical centers;

[0093] • the temporary schedule of the 107 medical personnel associated with the execution of each test;

[0094] • the results of the medical tests performed, including images, reports and measurements associated with patient 106;

[0095] • the historical data stored by means 105, comprising traceability of all actions performed in relation to each patient 106 and each medical protocol 111.

[0096] Based on this data / information, the aforementioned predictive analysis (carried out by the corresponding predictive processing means 113) is able to:

[0097] 1. predictively determine the progress status of clinical trial 110, including the percentage of medical tests completed within their respective time window, the recorded deviations and the temporal trend of compliance;

[0098] 2. Identify risks of non-compliance with one or more medical tests from the plurality defined by the medical protocol 111, based on patterns of unavailability of clinical centers 112, accumulated delays, successive reschedulings or lack of patient adherence 106;

[0099] 3. detect operational anomalies in the development of clinical trial 110, such as clinical centers that systematically delay the delivery of results, medical staff with operational overload that affects the scheduling of medical tests or medical tests whose execution presents recurrence of incidents;

[0100] 4. Evaluate the integrity of the medical protocol, verifying whether the actual sequence of medical tests, the times between tests, and the results generated by clinical centers 112 conform to the dependencies and specifications established in medical protocol 111;

[0101] 5. Derive aggregate datasets or analytical indicators (e.g., compliance indices, risk indicators, clinical center performance metrics, or patient adherence measures) that enable authorized users to have a consolidated view of the status of the clinical trial 110; 6. Issue notifications to one or more authorized users when the means 106 determine that a patient 106, a clinical center 112, a medical test, or a set of these elements are at risk of noncompliance with the medical protocol 111.

[0102] Additionally, the method, through the aforementioned predictive processing means 113 and from the result of the predictive analysis of the data / information obtained, is able to project future scenarios of trial compliance, estimate the operational or clinical impact of accumulated delays, anticipated unavailability or recurring incidents, in order to allow users to make corrective decisions before significant deviations occur in the outcome of the aforementioned trial.

[0103] The following are illustrative, non-limiting examples of the operation of the processing means 113 that carry out the algorithmic analyses on the data / information described above.

[0104] The aforementioned processing means 113 can detect that a set of medical tests corresponding to, for example, Visit 3 (third visit) of a patient has an accumulated delay of more than five days with respect to the time window defined in the medical protocol 111. Based on the historical patterns stored by the storage means 105, the processing means 113 can estimate that, if the trend continues, the clinical trial 110 could exceed the deviation threshold allowed in the corresponding phase.

[0105] Processing tools 113 can also identify that clinical center 112, responsible for performing imaging tests, experiences recurring rescheduling within a seven-day interval. Based on the temporary schedules of medical staff 107, processing tools 113 can determine that staff unavailability could lead to future missed appointment windows.

[0106] In another example, processing means 113 can detect that a patient 106 has missed two consecutive medical tests associated with their medical protocol 111. Based on the follow-up history, means 113 can determine an increased risk of clinical trial dropout 110 and generate a notification to the designated patient management user. In another example, means 113 can detect an inconsistency between laboratory results and the time parameters defined by medical protocol 111, determining that the actual sequence of medical tests may not align with the anticipated clinical dependencies. They can also, based on accumulated delays in scheduling PET scans at a clinical center 112, project that imaging equipment availability will be insufficient during the next critical window and generate an early alert to the user.

[0107] It should be understood that all media 101-104 described herein, including analytical processing media 113 and any other data processing and acquisition media, comprise a functional combination of hardware and software that enables them to perform the specific functionalities described in association with each. By way of example, but not as a limitation, these processing media may consist of one or more processors, microprocessors, central processing units (CPUs), graphics processing units (GPUs), application-specific integrated circuits (ASICs), and the corresponding programming logic. In turn, storage media 105, and any other storage media described herein, may comprise persistent or temporary memory hardware, such as RAM, ROM, flash memory, SSDs, or HDDs, configured to hold the information.Specifically, they may contain structured clinical trial databases (including protocols, patient history and results) and risk models used for predictive analysis, as well as store program instructions executed by the means 101-104,113 described herein.

[0108] In this text, the words "comprises", "includes" and their variants (such as "comprising", "including", etc.) should not be interpreted in an exclusionary way, that is, they do not exclude the possibility that what is described includes other elements, steps, etc.

[0109] Furthermore, the invention is not limited to the specific embodiments described but also encompasses, for example, those that can be carried out by the average person skilled in the art (for example, regarding the choice of materials, dimensions, components, configuration, etc.), within the scope of the claims.

Claims

CLAIMS 1. A device (100) for managing one or more clinical trials (110), comprising means (101) for digitally entering a data set (108) indicative of one or more medical protocols (111) associated with one or more clinical trials (110) relating to a pathology; wherein the device (100) is characterized in that it further comprises: means (102) for digitally associating each of the one or more medical protocols (111) with at least one clinical center (112) for the performance of at least one medical test relating to the corresponding medical protocol; means (103) for digitally scheduling a plurality of medical tests associated with each of the one or more medical protocols (111) such that each medical test has an associated time window for the performance of the respective medical test;and means (104) for digitally associating a patient (106) with a medical protocol (111) and one or more device users in such a way that the one or more device users (100) are registered and notified to perform one or more tests from the plurality of tests of the respective medical protocol (111) according to the respective time window.; 2. The device (100) according to claim 1, wherein the data set (108) indicative of one or more medical protocols (111) associated with one or more clinical trials (110) relating to a pathology comprises at least one of: a clinical trial identification code (110), a patient identification code (106) of the clinical trial (110), a phase of the clinical trial (110), and information relating to the clinical trial (110), including the medical protocol (111) associated with the clinical trial (110).

3. The device (100) according to claim 1 or 2, wherein the means (102) for digitally associating each of the one or more medical protocols (111) to at least one clinical center (112) are further configured to associate at least one group of authorized medical tests for each medical protocol (111).

4. The device (100) according to any one of the preceding claims, wherein the means (103) for digitally planning the plurality of medical tests associated with each of the one or more medical protocols (111) are configured to determine a series of medical tests for each patient (106) based on a patient pathology and clinical trial (110).

5. The device (100) according to claim 4, wherein the means (103) for digitally planning the plurality of medical tests associated with each of the one or more medical protocols (111) are configured to generate alerts associated with the time window of each of the tests.

6. The device (100) according to claim 4, wherein the means (103) for digitally planning the plurality of medical tests associated with each of the one or more medical protocols (111) are configured to associate an independent time schedule to each medical test to be performed on the same patient (106).

7. The device (100) according to any one of the preceding claims, further comprising means for storing results of medical tests performed on the patient (106).

8. The device (100) according to claim 7, wherein the storage media are configured to store at least one of patient images (106) and patient medical reports (106), associated with a patient identifier (106) and with a corresponding clinical trial identifier (110).

9. The device (100) according to claim 7 or 8, wherein the storage means are configured to store a history of medical tests performed on each patient (106).

10. The device (100) according to any one of the preceding claims, comprising means for displaying images associated with medical tests performed on the patient (106).

11. The device (100) according to any one of the claims, comprising digital communication means with one or more medical devices.

12. The device (100) according to any one of the preceding claims, comprising a graphical user interface.

13. The device (100) according to any one of the preceding claims, comprising predictive processing means (113), preferably based on Artificial Intelligence (AI), wherein the predictive processing means (113) comprises a processing unit executing at least one predictive model, preferably AI-based, and configured to: predictively process, by means of a processing unit executing at least one predictive model, the data generated by the means (101, 102, 103, 104) in order to obtain metrics that quantify a degree of compliance with the clinical trial's medical protocol; and determine an operational status of the clinical trial based on said compliance metrics and operational data;and anticipate potential risks or clinical or operational deviations based on the operational status of the clinical trial, compliance metrics, and operational data before they occur, generating alerts.

14. The device (100) according to claim 13, wherein the predictive processing means (113) are configured to further process data stored in storage means (105) comprising: the data set (108), data relating to associations between medical protocols with clinical centers, data relating to the planning of medical tests and the association of patients to medical protocols and users of device 100.

15. A computer-implemented method (300) for managing one or more clinical trials, comprising the steps of: digitally entering (301) a set of data indicative of one or more medical protocols associated with one or more clinical trials relating to a pathology; digitally associating (302) each of the one or more medical protocols with at least one clinical center for the performance of at least one medical test relating to the corresponding medical protocol; (303) digitally plan a plurality of medical tests associated with each of one or more medical protocols in such a way that each medical test has an associated time window for the performance of the respective medical test; and (304) digitally associate a patient to a medical protocol and one or more device users in such a way that the one or more device users are registered and notified to perform one or more tests from the plurality of tests of the respective medical protocol in accordance with the respective time window.

16. The method (300) according to claim 15, wherein the data set indicative of one or more medical protocols associated with one or more clinical trials relating to a pathology comprises at least one of: a clinical trial identification code, a clinical trial patient identification code, a clinical trial phase, and information relating to the clinical trial, including the medical protocol associated with the clinical trial.

17. The method (300) according to claim 15 or 16, comprising associating at least one group of authorized medical tests for each medical protocol.

18. The method (300) according to any one of claims 15-17, comprising determining a series of medical tests for each patient based on a patient pathology and the clinical trial.

19. The method (300) according to claim 18, comprising generating alerts associated with the time window of each of the tests.

20. The method (300) according to claim 18, comprising associating an independent time schedule to each medical test to be performed on the same patient.

21. The method (300) according to any one of claims 15 to 20, storing at least one of the patient's images and medical reports, associated with a patient identifier and with a corresponding clinical trial identifier and, preferably, a history of medical tests performed on each patient.

22. The method (300) according to any one of claims 15 to 21, comprising visualizing, by means of visualization, images associated with medical tests performed on the patient.

23. The method (300) according to any one of claims 15 to 22, comprising performing, by means of a predictive processing module (113), preferably an Artificial Intelligence (AI)-based processing module, the following steps: predictively processing, by means of a processing unit executing at least one predictive model, preferably AI-based, the data generated by the means (101, 102, 103, 104) in order to obtain metrics that quantify a degree of compliance with the clinical trial's medical protocol; determining an operational status of the clinical trial based on said compliance metrics and operational data; and anticipating possible clinical or operational risks or deviations based on the operational status of the clinical trial, the compliance metrics, and the operational data before they occur, generating alerts.

24. The method (300) according to claim 23, wherein the step of predictively processing the data comprises further predictively processing data that is stored on storage media, wherein said data comprises at least one of the following: the data set, data relating to associations between medical protocols with clinical centers, data relating to the planning of medical tests and the association of patients to medical protocols and users of the device.

25. The method (300) according to any one of the preceding claims, wherein the method comprises: processing, by means of an AI-based medical image processing module, medical images obtained from medical tests performed on the patient during the clinical trial; performing, by means of the AI-based processing and diagnostic module, an automated clinical diagnosis of the patient from the processed medical images; associating the clinical diagnosis obtained with the patient.

26. The method (300) according to claim 25, wherein the image processing step comprises at least one of the following sub-steps: a) automatically measuring lesion diameters according to oncological standards, preferably according to RECIST or RECIST standards; b) segmenting and calculating a tumor volumetry associated with the lesions; and / or c) obtaining advanced functional parameters, such as SUVmax or TLG, from positron emission tomography (PET) images of the lesions.

27. The method (300) according to claim 25 or 26, comprising validating, by means of the AI-based medical image processing module, the conformity of the medical image acquisition with the medical trial protocol, wherein the validation comprises: automatically detecting deviations in critical technical parameters of the medical image; and verifying consistency of image acquisition between multiple centers, ensuring the integrity and technical quality of the clinical data obtained.

28. The method (300) according to any one of claims 25 to 27, comprising: predicting, by means of the AI-based medical image processing module, a disease progression or a patient's response to a treatment being applied, from a radiomic analysis of the patient's medical images; and generating a Clinical Risk Indicator for the patient from the generated predictions; anticipating possible clinical or operational risks or deviations based on the operational status of the clinical trial, compliance metrics, operational data and the Clinical Risk Indicator before they occur.

29. The method (300) according to claim 28, wherein the step of anticipating possible risks, clinical or operational deviations comprises performing an automatic prioritization of cases by combining the derived Clinical Risk Indicator with a set of operational risks of the clinical trial, which include at least delays or non-compliance with the clinical trial, to generate alerts about potential deviations from the medical protocol associated with the clinical trial.

30. A computer program comprising instructions that, when the program is executed by a processor, cause the processor to carry out the steps of the method of any one of claims 15 to 29.

31. A computer-readable storage medium comprising instructions that, when executed by a processor, cause the processor to carry out the steps of the method of any one of claims 15 to 29.

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