This capstone project focuses on leveraging Large Language Models (LLMs) to automate the identification and prioritization of strategic partnerships in the digital health sector. The industry is hindered by fragmentation and inefficient, manual processes for detecting synergies. By analyzing both structured and unstructured data from over 4,000 digital health companies cataloged by Digital.Health, we aim to develop an AI-driven ‘Synergy Discovery Engine’ that automatically detects meaningful relationships between companies. This system will convert unstructured company data into structured synergy scores, enabling more efficient decision-making for commercialization and accelerating collaboration. Ultimately, this approach aims to close the clinical translation gap by enabling faster adoption of breakthrough healthcare solutions.
Watch the team present this project at 01:31:02 in the session recording here.
Keywords: LLM, healthcare innovation, synergy detection, automation, clinical AI, data enrichment, partnership discovery, commercialization pathway
Faculty Advisor
Ashish Pujari is a Data and AI consultant, practitioner, and educator with over 20 years of experience in in machine learning, big data, and cloud computing. He has led large global technology and data science teams and consulted for Fortune 500 companies in banking, finance, healthcare, insurance, and manufacturing.
As a Principal ML/AI Architect at AWS, Ashish provides strategic guidance to enterprise customers on leveraging the cloud for AI and Machine Learning. Prior to joining Amazon, he served in various technology leadership roles at Credera, GLG, IRI, and Pegasystems. Ashish holds a Master of Science in Analytics from the University of Chicago and BS in Electrical Engineering from the National Institute of Technology, Rourkela.