Convenors : Dr. Arindam Maitra and Dr. Rutuja Patil
Team members – Dr. Bikash Santra, Dr. Bharat Kumar Padhar, Dr. Pragyan Acharya, Dr. Dhiraj Agarwal, Dr. Saketh Ram Thrigulla, Dr. Pankaj Bhardwaj, Dr. Mona Duggal, Dr. Asif Iqbal, Dr. Rajendra Nagar, Dr. Pankaj Yadav
Volunteer – Aditi Joshi
Key Pointers:
How will the Multimodal AI recommendation engines fuse clinical, omic, and lifestyle data to provide health advice that is both personalized and evidence-based?
What are the key strategies for integrating the PRISM platform into large-scale public health cohorts (like VADU or Garbhini) to ensure cultural and geographical scalability?
How can retrospective data from specialized health initiatives be leveraged to validate the predictive power of "Digital Twins" in integrative medicine contexts?
This session focuses on the scalability and real-world deployment of the PRISM platform. By integrating multimodal AI with extensive public health cohorts, this session outlines the transition from individual "Digital Twins" to a population-level framework for precision medicine and national health policy.
Key Discussion Points:
AI for Multimodal Analysis & Recommendation Engines: Developing sophisticated AI architectures capable of fusing diverse data streams (clinical, omic, and lifestyle) to generate interpretable, personalized medical recommendations and preventive case scenarios.
Large-Scale Integration in Public Health Cohorts: Exploring strategies to deploy PRISM within established health networks, such as the VADU Ayurgenomics cohort and the Indepth network, to ensure geographical and cultural scalability.
Leveraging Retrospective Cohorts for Integrative Medicine: Utilizing existing data from specialized health initiatives—including Garbhini (maternal health), ICGA (cancer genomics), MASLD, ICMR, and CCRAS—to validate the predictive power of Digital Twins in specific disease contexts.
Building Interoperable Platforms: Designing the software architecture that allows these knowledge resources to integrate seamlessly with the Digital Twin models developed in previous sessions.