Overview
AI is opening new pathways to improve diagnostic accuracy, timeliness, and equity, particularly for high-risk and underserved populations. Join us for this webinar spotlighting two research initiatives from 2024-2025 National Academy of Medicine (NAM) Scholars in Diagnostic Excellence who are pushing the boundaries of safe, fair, and effective AI in healthcare.
- Equity-Grounded AI for Peripheral Arterial Disease (PAD): Researchers are fine-tuning large language models to detect PAD from electronic health record data, aiming for accurate, safe, and equitable performance across diverse patient groups. Hear from Andrew Gonzalez, MD, JD, PhD, about this project seeking scalable diagnostic support to help prevent avoidable amputations without compromising fairness.
- AI Diagnostic Decision Support for Older Adults in Primary Care: This project develops deep learning models trained on clinician behaviors to improve diagnostic safety for older adults. Gary Weissman, MD, MSHP, will explore a randomized chart review that compares AI-generated and human diagnoses to evaluate accuracy, safety, and readiness for clinical use.
Date
Wednesday, August 27, 2025Time
Organizers
Speakers
Andrew Gonzalez, MD, JD, PhD
Assistant Professor of Surgery, Indiana University School of Medicine
Dr. Gonzalez is a practicing vascular surgeon and research scientist. His clinical focus is peripheral arterial disease, advanced open, endovascular, and hybrid aortic interventions, and vascular trauma. His research focus is on applications of AI/ML and network theory to chronic non-communicable disease care in complex adaptive systems.
Gary Weissman, MD, MSHP
Assistant Professor of Medicine and Informatics, University of Pennsylvania Perelman School of Medicine
NAM Scholar of Diagnostic Excellence (2024-2025)
Dr. Weissman is a physician and cares for people in the medical intensive care unit at the Hospital of the University of Pennsylvania. He runs the Clinical Artificial Intelligence and Machine Learning (CAIML) Lab in the Palliative and Advanced Illness Research (PAIR) Center, where he develops and evaluates AI-based decision support systems.
Anjana Sharma, MD
Learning Hub Faculty Lead, UCSF CODEX
Associate Professor, UCSF Department of Family and Community Medicine
Anjana brings a wealth of experience in patient safety, quality improvement, patient engagement, medical education, and health equity. As a primary care physician and researcher, she has spent numerous years studying patient safety in primary and ambulatory care, researched strategies to prevent diagnostic and medication errors, and developed interventions to improve patient engagement in medical care. She also teaches evidence-based medicine and patient involvement in practice improvement to the UCSF Family and Community Medicine Residency Program at ZSFG.
Learn more about Anjana here.