Penn AI Seminar featuring René Vidal (University of Pennsylvania)
AI in Health: Foundation Models for Radiology, Cardiology and Autism Diagnosis
This talk explores how AI can transform medical data into clinically meaningful predictions and assessments across radiology, cardiology, and autism diagnosis. I will begin with foundation models for chest radiology, highlighting methods that transform free-text reports into structured clinical facts, enable interpretable query-based classification, and support long-tailed and zero-shot disease recognition. I will then introduce EchoRG, a grounded foundation model for echocardiography that combines structured prediction with factual, interpretable report generation, and discuss emerging biomarkers such as cardiac biological age. Finally, I will present CAMI‑2DNet, a video-based system that learns disentangled motion representations to enable scalable, annotation-free assessment of motor imitation in autism. Together, these works illustrate how advances in representation learning and multimodal modeling can address distinct clinical needs, from recognizing disease and generating reports to quantifying human behavior.
Sponsored by:
Speaker
René Vidal
René Vidal is the Penn Integrates Knowledge and Rachleff University Professor of Electrical and Systems Engineering and Radiology at the University of Pennsylvania, where he directs the Center for Innovation in Data Engineering and Science (IDEAS) and serves as Co-Chair of Penn AI. He is also an Amazon Scholar, Affiliated Chief Scientist at NORCE, and former Associate Editor-in-Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence. Professor Vidal’s research advances the mathematical foundations of deep learning and trustworthy AI, with broad impact across computer vision and biomedical data science. His contributions have been recognized with major honors, including the IEEE Edward J. McCluskey Technical Achievement Award, the D’Alembert Faculty Award, the J.K. Aggarwal Prize, the ONR Young Investigator Award, the NSF CAREER Award, and best paper awards in machine learning, computer vision, signal processing, control, and medical robotics. He is a Fellow of ACM, AIMBE, IEEE, and IAPR, and a Sloan Fellow.