Platforms Development for implementation and scaling
Platforms Development for implementation and scaling
Co-Convenors: Dr. Asif Ekbal, Dr. Pranjal Borah and Dr. Naveen Varshneya
Discussants – Dr. Deeksha Varshney, Dr. Divya Saxena, Dr. Vignesh Muralidharan, Dr. Rakesh Narayanan, Dr. Sumit Kalra, Dr. Rutuja Patil, Dr. Dhiraj Agarwal, Dr. Ishan Gupta
Volunteer – Rishabh Jain
Key pointers :
What are the critical technical and infrastructure hurdles for deploying a Prakriti-informed platform at a national scale?
How should Human-Machine Interfaces (HMI) be designed to allow both practitioners and patients to interact naturally with complex integrative data?
How can we best integrate Multimodal Analysis, LLMs, and Small Language Models (SLMs) to create a robust, trustworthy recommendation engine?
This breakout session focuses on the translational architecture required to take PRISM from a laboratory concept to a large-scale public health reality. Participants will deliberate on the human-centric and computational interfaces needed for a seamless user experience.
Key Deliberation Points:
Life-Course Implementation: Strategizing the deployment of PRISM across diverse clinical settings, from antenatal and pediatric to geriatric care, ensuring relevance for both clinical practice and public health.
Anukta Collective & Knowledge Engineering: Evaluating the use of AI and NLP to crowdsource and digitize "Anukta" (unspoken or undocumented) Ayurvedic knowledge.
Defining the structure for Knowledge Graphs to power GenAI-enabled interoperable platforms.
Human-Machine Synergy: Developing Human Machine Interfaces (HMI) for Conversational AI, allowing practitioners and patients to interact naturally with the system.
Deliberating on the integration of Multimodal Analysis, LLMs, and SLMs (Small Language Models) to create robust Generative AI tools.
Intelligence & Recommendation Engines: Designing the logic for the platform's Recommendation Engines to provide actionable, evidence-based wellness insights.
Large-Scale Implementation Roadmap: Identifying the infrastructure and technical hurdles for national-level deployment and sustainable scaling.