• Nirup Kumar Reddy Pothireddy

This research introduces a GenAI-driven digital twin framework that simulates personalized cardiovascular dynamics based on real-time data from wearable sensors, imaging, and historical EHRs. The system models the effects of lifestyle interventions, medication adherence, and surgical outcomes on conditions like coronary artery disease and hypertension. Integrated with healthcare apps, it empowers clinicians to visualize projected health trajectories, while patients gain intuitive feedback on long-term decisions. The research incorporates generative artificial intelligence into digital twin models to conduct time-sensitive simulations of person-specific cardiovascular data systems. The model relies on transformer-based architectures which use echocardiograms together with hemodynamic records and genomics to predict unique patient responses to different clinical situations during multistep training. The digital twin receives real-time updates concerning physiological and behavioral data because it connects to cloud-based EHR systems and wearable IoT devices. Healthcare providers can use this capability to spot potential risks before they occur and run simulated treatment strategies and make successful proactive changes. Medical practitioners achieved diagnostic precision growth of 26% and decreased hospitalization dangers by 19% through testing these procedures in heart clinics. Through the new system healthcare providers achieved better interaction with patients for decision-making purposes which promoted unique treatment strategies and stronger patient involvement. The system implements three essential components which consist of explainability features to boost confidence levels and federated learning technology for privacy safeguards and a user-friendly interface for inter-specialty teamwork. Clinical practice has shifted toward precise cardiovascular medicine through predictive patient care because of this digital twin system developed using artificial intelligence. The research findings establish fundamental knowledge needed to work toward better multi-organ digital twin platforms and real-time public health simulation solutions that can deliver enhanced chronic disease treatment methods.

Generative Artificial Intelligence (GenAI), Digital Twin in Healthcare, Cardiovascular Disease Simulation, Personalized Medicine, Real-Time Health Monitoring

"GENAI-POWERED DIGITAL TWIN FOR CARDIOVASCULAR DISEASE SIMULATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.g920-g934, February-2024, Available :http://www.jetir.org/papers/JETIR2402715.pdf

"GENAI-POWERED DIGITAL TWIN FOR CARDIOVASCULAR DISEASE SIMULATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppg920-g934, February-2024, Available at : http://www.jetir.org/papers/JETIR2402715.pdf