AI for Clinical Trials and Precision Medicine - Ruishan Liu

Explore how artificial intelligence can revolutionize clinical trials and precision medicine in this 56-minute Stanford University lecture by Ruishan Liu. Discover Trial Pathfinder, a computational framework that optimizes cancer trial designs through synthetic patient cohort simulations. Learn about inclusive criteria and data valuation techniques that benefit diverse patients and trial sponsors. Delve into methods for quantifying cancer therapy effectiveness in patients with specific mutations, demonstrating how large-scale real-world data analysis generates insights and hypotheses for precision oncology. Gain valuable knowledge on the intersection of machine learning and human disease applications, health, and genomics from this postdoctoral researcher's expertise in biomedical data science.

Intro
Al for Medicine
Al for Clinical Trials
Strict Eligibility is a Barrier to Trial Enrollment
How Clinical Trial Eligibility Criteria are Decided?
Trial Pathfinder
Shapley Analysis
Data-driven criteria enlarges pool of eligible patients while reducing HR
Relaxing threshold
Piloted by Roche to Guide Cancer Trial Design
Precision Medicine for Oncology
Implementation on real-world clinico-genomics data
Case Example: Mutation-Treatment Interactions
Systematic pan-cancer analysis of mutation-treatment interactions