Pratik Shah
Faculty member at the University of California
Dr. Pratik Shah is a pioneering researcher advancing the integration of artificial intelligence, deep learning, and machine learning into medical diagnostics and clinical development. A professor at the University of California, he holds faculty appointments in the departments of Pathology & Laboratory Medicine, Electrical Engineering & Computer Science, and Biomedical Engineering. Dr. Shah's research integrates AI, statistics, engineering, and biomedical science to advance the diagnosis and treatment of cancer, infectious diseases, and neurological disorders. His innovations include AI-powered computational staining for cancer diagnosis, non-invasive diagnostic medical devices, and deep reinforcement learning models for optimizing drug dosages—all aimed at reducing healthcare costs and improving patient outcomes. His work focuses on real-world biological and clinical insights and cost-effective solutions to enhance accessibility, precision, and efficiency in healthcare.
Dr. Shah is an expert in the clinical translation of research findings, particularly in real-world deployment platforms designed to provide equitable and affordable healthcare. Research from his lab has been published in Cell Press, Nature Digital Medicine, and Journal of the American Medical Association.
A strong advocate for the ethical and regulatory advancement of AI in medicine, Dr. Shah actively contributes to discussions shaping AI policies, ensuring that healthcare AI solutions are fair, transparent, and reliable. His work has influenced industry collaborations and frameworks designed to establish responsible AI practices in clinical settings. He serves as an expert reviewer for national grant panels on emerging technologies and as an advisor on responsible AI adoption and deployment.
Previously, Dr. Shah was a Principal Investigator at the Massachusetts Institute of Technology (MIT), where he led a computational medicine research program at the MIT Media Lab. He also served as the principal investigator on a memorandum of understanding with the U.S. FDA to help establish AI and machine learning research ecosystems for clinical development. Dr. Shah holds a BS, MS, and PhD in biological and data sciences and completed fellowship training at Massachusetts General Hospital, the Broad Institute of MIT and Harvard, and Harvard Medical School.
Topics
AI in Biotech
AI in biotech is evolving rapidly—but understanding where it actually delivers value requires cutting through both buzz and skepticism. In this talk, Dr. Pratik Shah distills over a decade of research and applied experience to reveal how machine learning and generative AI are already transforming drug discovery and clinical development. He unpacks key advances—like AI-powered medical image analysis and real-world data modeling—and outlines what it takes to move from experimentation to deployment. Audiences will leave with a grounded view of where AI is driving real impact, what still needs to be solved, and how to assess emerging technologies critically.
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Designing Clinical Trials with AI
The clinical trial pipeline is being reimagined with the help of AI—from trial design to patient selection to real-time monitoring. Drawing on case studies and research from his lab, Dr. Shah explores how predictive modeling and deep learning with statistical validation are streamlining this historically slow and costly process. He also addresses regulatory science and the importance of model explainability. This talk is ideal for R&D leaders, regulatory teams, and innovation officers navigating the next phase of digital transformation in pharma.
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AI in your Organization
In this session, Dr. Shah shares a strategic framework for how life sciences and health technology companies can become AI-enabled. Topics include developing internal AI ecosystems, onboarding cross-functional talent, and creating infrastructure to support continuous learning and ethical deployment. He also discusses strategies for adapting to the rapid evolution of AI tools, ensuring both scientific rigor and real-world readiness. Attendees will gain actionable insights for maintaining competitive advantage in AI-driven outcomes.
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