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AI for Clinical Trials and Precision Medicine

ORAL · Invited

Abstract

Toward a new era of medicine, our mission is to benefit every patient with individualized medical care. This talk explores how AI can make precision medicine more effective and diverse. I will first discuss Trial Pathfinder, a computational framework to optimize clinical trial designs (Liu et al. Nature 2021). Trial Pathfinder simulates synthetic patient cohorts from medical records, and enables inclusive criteria and data valuation. In the second part, I will discuss how to leverage large real-world data to identify genetic biomarkers for precision oncology (Liu et al. Nature Medicine 2022, Liu et al. Nature Communications 2024), and how to use language models to form individualized treatment plans (Liu et al. Cell Reports Medicine 2024).

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Publication: 1. Liu, Ruishan, et al. "Evaluating eligibility criteria of oncology trials using real-world data and AI." Nature 592.7855 (2021): 629-633.<br>2. Liu, Ruishan, et al. "Systematic pan-cancer analysis of mutation–treatment interactions using large real-world clinicogenomics data." Nature Medicine 28.8 (2022): 1656-1661.<br>3. Liu, Ruishan, et al. "Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients." Cell Reports Medicine 5.3 (2024).<br>4. Liu, Ruishan, et al. "Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers." Nature Communications 15.1 (2024): 10884.

Presenters

  • Ruishan Liu

    University of Southern California

Authors

  • Ruishan Liu

    University of Southern California