Sponsor
Biostatistics and Bioinformatics
Co-Sponsor(s)
AI Health; BERD Core; Computational Biology and Bioinformatics (CBB); Master of Management in Clinical Informatics (MMCi); Statistical Science
Reimagining Clinical Trials with AI Agents
Friday, February 13,
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Speaker(s): Jimeng Sun
Abstract: What if clinical trials could learn from all prior evidence, anticipate risk earlier, and guide better decisions before execution? In this talk, I will present a forward-looking view of AI agents for clinical trials, highlighting recent advances in evidence synthesis, trial representation, patient-trial matching, and outcome prediction. Based on peer-reviewed research and deployed systems, I will share where AI is already creating value, what challenges remain, and how these technologies may evolve to support statistical and analytical workflows in the near future. The session blends aspiration with practical insights and a brief product demonstration.
Bio: Jimeng Sun is a Professor of Computer Science at the University of Illinois Urbana-Champaign and Co-Founder & CEO of Keiji AI. His research focuses on AI and machine learning for clinical research and drug development, with publications in Nature Communications, npj Digital Medicine, NeurIPS and other leading venues. He works closely with biopharma and healthcare organizations to translate AI research into real-world clinical trial systems.
Zoom Link: https://duke.zoom.us/j/94817002913?pwd=a4LOKsKKXqHv4A5eGjX2QSp6DEXA0Z.1
Meeting ID: 948 1700 2913
Passcode: 103794
Bio: Jimeng Sun is a Professor of Computer Science at the University of Illinois Urbana-Champaign and Co-Founder & CEO of Keiji AI. His research focuses on AI and machine learning for clinical research and drug development, with publications in Nature Communications, npj Digital Medicine, NeurIPS and other leading venues. He works closely with biopharma and healthcare organizations to translate AI research into real-world clinical trial systems.
Zoom Link: https://duke.zoom.us/j/94817002913?pwd=a4LOKsKKXqHv4A5eGjX2QSp6DEXA0Z.1
Meeting ID: 948 1700 2913
Passcode: 103794
