Topic: The Digital Shift: Transforming Clinical Trials with Big Data and AI
Speaker: PhD degree in Biomedical Informatics from Stanford University
Time : May 05, 2026 (Tuesday), 2:00 – 4:00 p.m.
Venue: The Management Building, 11F, AI Lecture Hall
Join Online: https://reurl.cc/WbvX79 or scan QR code on poster
About the Speaker:
Dr. Ray Lin got his BS and MS degrees in Computer Science and
Information Engineering from National Taiwan University, specializing in AI in
his Master’s Thesis. He later obtained a MS degree in Statistics and a PhD
degree in Biomedical Informatics from Stanford University. His PhD research
focused on synthetic clinical trials using computer-simulated virtual patients
to evaluate cancer screening for lung cancer and breast cancer. Dr. Lin has
accumulated 15 years of experience in drug development with Genentech (Roche)
supporting clinical trials for developing new cancer treatments, including
therapies approved in multiple cancer types globally. He has also been actively
engaging in cross-industry collaborations and professional organizations,
including multiple leadership positions in an American Statistical Association
(ASA) scientific working stream, a US FDA initiated cross-pharma initiative in
oncology, and the ASA San Francisco Bay Area local chapter.
Abstract:
Randomized controlled trials (RCTs) remain the gold standard for clinical research; however, they face significant hurdles, including high costs, lengthy timelines, and ethical or feasibility constraints. The era of Big Data and AI presents transformative opportunities to address these challenges. Real-world data (RWD)—such as electronic medical records, insurance databases, and disease registries—can supplement RCTs to reduce costs, enhance enrollment, and accelerate timelines, particularly for rare diseases with high unmet medical needs. Simultaneously, AI-driven models can predict patient outcomes, enabling the simulation of "virtual patients" in synthetic clinical trials when traditional randomization is impractical or unethical.
This presentation features two case studies: (1) A novel dynamic borrowing method that integrates RCTs with external data to increase study efficiency while minimizing selection bias and maintaining trial integrity; (2) The use of virtual patients and synthetic trials to evaluate cancer screening programs. These findings were incorporated into the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network consortium (CISNET) consortium and have directly influenced breast cancer screening recommendations in the U.S. and Taiwan.
Organizers: College of Intelligent Computing
& Artificial Intelligence Research Center
※ No registration needed.