MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123132 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Vishwavidyapeeth; Malla Reddy University; Malla Reddy College Of Engineering And Technology; Malla Reddy Engineering College For Womens; and Dr. Shivaprasad S, Medchal-Malkajgiri, Telangana, on Dec. 6, 2025, for 'digital twin enhanced ai system for predictive healthcare analytics.'
Inventor(s) include Dr. Shivaprasad S; Mrs. Sai Kumari Thakur; Dr. G. Jose Moses; Dr V Madhusudhana Reddy; Mr. K. V. Rajesh; and Dr Ramu Vankudoth.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "Healthcare environments create immense data from patients in the form of patient clinical records, diagnostic tests, wearable sensors, and medical imaging. Managing this information in a meaningful way requires tools that are able to combine real time monitoring with long term historical insight. A digital twin enhanced AI system generates a virtual copy of each patient with physiological characteristics, medical history, lifestyle traits and continuous feeds from sensors. This virtual representation changes over time, reflecting not only the patient's changing health condition but also keeping a structured history of past trends.The digital twin can form part of a basis for predative healthcare analytics. Real-time data enters the model, where artificial intelligence algorithms search for any early deviations from normal behavior, and potential risks are detected relating to chronic diseases, while any risks of deterioration are forecasted before symptoms become visible clinically. Risk assessments based on the model support proactive intervention in which healthcare providers could change their treatment plans, make lifestyle recommendations, or arrange for follow-up examination.The way in which we see a method used by clinicians is to simulate different treatment scenarios in the digital twin environment in which one can observe projected outcomes without at the same time exposing the patient to unnecessary risk. Over time, the model increases its accuracy as feedback is added from actual clinical results and health results. This constant refinement ensures that predictions are constantly reliable even when the patient's condition changes.The system enables the use of digital twin modelling design to build the capability of remote care monitoring, hospital decision-making and personalized health management by leveraging adaptive AI analytics. This translates into earlier identification of health risks, better engagement of the patients, and more efficient use of medical resources."
Disclaimer: Curated by HT Syndication.
