MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641097717 A) filed by Velammal College Of Engineering And Technology on August 12, 2026, for Ai-Based Adaptive Healthcare Diagnosis And Treatment Recommendation System.
Inventors include Mr. P. Kalyanakumar; Dr. T. Venkatesh Kanna; Mrs. A. Periyanayaki; Mrs. J. John Shiny; Mrs. M. Ishvarya; Dr. S. Esakki Muthu; and Mr. K. Loganathan.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: AI-based Adaptive Healthcare Diagnosis and Treatment Recommendation System ABSTRACT The present invention relates to an AI-based adaptive healthcare diagnosis and treatment recommendation system configured to process heterogeneous patient-specific medical data and generate clinical decision-support outputs. The system acquires information from electronic health records, laboratory systems, medical imaging devices, physiological monitors, wearable sensors, medication records, and patient history. A preprocessing and multimodal data fusion module converts the acquired information into a unified patient representation. An artificial intelligence inference engine analyzes the representation to determine probable health conditions, risk levels, disease severity, and prediction confidence. A personalized treatment recommendation module generates ranked treatment-support options based on patient characteristics, comorbidities, allergies, medications, previous treatment response, and identified clinical constraints. A safety-validation module evaluates contraindications, drug interactions, and other patient-specific risk factors before recommendations are presented to an authorized healthcare professional. An explainable AI module identifies influential clinical parameters associated with generated predictions. An adaptive learning module incorporates updated patient data, treatment response, outcomes, and validated clinical feedback to recalibrate or update subsequent diagnostic and recommendation outputs, thereby enabling continuous patient-specific clinical decision support.
Disclaimer: Curated by HT Syndication.
