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Postdoctoral Applicants
Bridging the Gap: Building Medical AI that Meets Clinical Reality
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Speaker: Greg Holste (UT Austin) Ph.D. Candidate The University of Texas at Austin Friday, February 20, 2026 3:00PM - 4:00PM and via Webcast: https://yale.zoom.us/j/93776021315 |
Zoom Password: 123
Abstract: Artificial intelligence (AI) has enabled breakthroughs in the analysis of medical images, electronic health records, and diverse sources of health data. However, most medical AI systems can only answer narrow questions like “Does this patient have based on this single image?” This framing fails to capture how medicine is actually practiced. Clinicians integrate information across modalities, reason over noisy longitudinal data, and assess the presence and future risk of multiple conditions at once—our AI models should too. In this talk, I present efforts to bridge this gap between academic AI and clinical reality with contributions spanning cardiology, ophthalmology, and radiology.
I will start by presenting my work building AI systems to interpret echocardiograms, the most widely used cardiac imaging tool. I will trace the evolution from single-view aortic stenosis detection to PanEcho, a multi-task model that fully automates echocardiogram interpretation by integrating multiple views like a cardiologist. Next, I address the temporal nature of progressive eye disease monitoring with an approach for disease prognosis based on irregularly-spaced longitudinal imaging, matching the clinical workflow. I will then discuss the long tail of abnormal findings on chest X-ray and the promise of vision-language models for zero-shot rare disease detection. I conclude with a vision for multimodal health monitoring, AI-driven clinical knowledge discovery, and prospective trials of human-AI collaboration to understand how these systems can lift ongoing burdens on the healthcare system and improve patient outcomes.
Bio: Greg Holste is a PhD Candidate in Electrical and Computer Engineering at The University of Texas at Austin, NSF Graduate Research Fellow, and MIT Tech Review 35 under 35 semifinalist. His research bridges artificial intelligence (AI) and healthcare, developing deep learning methods to solve clinical decision-making and medical image analysis problems. Greg collaborates with clinicians to develop AI systems that help physicians interpret complex health data more quickly and accurately with innovations in multimodal, self-supervised, and long-tailed learning. His research has been published in journals such as JAMA, JAMA Cardiology, Nature Communications, The Lancet Digital Health, among others, and presented at top conferences including CVPR, ICML, ICCV, and MICCAI.
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