Integrative System and Method for Performing Medical Diagnosis Using Artificial Intelligence

An integrative system (100) for performing medical diagnosis using artificial intelligence, the integrative system (100) comprising:

• a hardware processor (108);
• and
• a memory (102) coupled to the hardware processor (108), wherein the memory (102) comprises a set of program instructions in the form of a plurality of subsystems, configured to be executed by the hardware processor (108), wherein the plurality of subsystems comprises:
• a medical input data collection subsystem (110) configured to collect patient information and phenotypic features associated with a patient from a plurality of medical devices, computer vision techniques, and from a conversational artificial intelligence questionnaire, wherein the phenotypic features comprise anatomic features, physical, physiological features, psychological features;
• a baseline health computation subsystem (112) configured to:
• apply the collected patient information and phenotypic features associated with the patient on to a trained machine learning model;
• generate baseline health status of the patient based on the results of the application of the trained machine learning model, wherein the generated baseline health status comprises a set of integrative medicine system parameters, wherein the set of integrative medicine system parameters comprise a set of traditional medicine system and a set of modern medicine system parameters of the patient;
• a baseline change determination subsystem (114) configured to:
• obtain a combination of:
• responses for a set of adaptive and conversational artificial intelligence-based questionnaire in real time from the patient;
• one or more real time patient information from the plurality of medical devices associated with the patient, one or more real time spatiotemporal information data associated with the patient, and one or more real time inputs from biochemical markers, clinical markers and multi-omics markers;
• estimate real time set of integrative medicine system parameters by applying the obtained responses for the set of adaptive and conversational artificial intelligence-based questionnaire, the obtained one or more real time patient information, the obtained one or more real time spatiotemporal information data and the obtained one or more real time inputs from biochemical markers, clinical markers and multi-omics markers onto the trained machine learning model;
• determine changes in the generated baseline health status of the patient by comparing the estimated real time set of integrative medicine system parameters with the estimated set of integrative medicine system parameters associated with the generated baseline health status by using artificial intelligence-based comparison model;
• and
• a health, wellness, and disease risk score computation subsystem (116) configured to:
• generate a health, wellness, and disease risk score of the patient based on determined changes in the generated baseline health status of the patient;
• and
• generate a recommendation message to the patient based on the generated health, wellness, and disease risk score.