I am a cardiologist and researcher in Division of Research at Kaiser Permanente Northern California. I am also an adjunct Assistant Professor at the Smidt Heart Institute at Cedars-Sinai. As a physician-scientist and statistician with focus on cardiology and imaging, I majored in statistics at Rice University, obtained my MD at UCSF, and received post-graduate medical education in internal medicine, cardiology, and a postdoc in computer science and biomedical data science at Stanford University. I am also a Deputy Editor at NEJM AI and Chair of the ImageGuideRegistry for the American Society of Echocardiography. Our mission is to improve cardiovascular diagnosis and disease detection using computer vision and artificial intelligence. Our group works on applications of deep learning, computer vision, and the statistical analysis of large datasets within cardiovascular medicine. We focus on AI model development (see section 'Precision Phenotyping'), AI model understanding (see section 'Interpretable Medical AI'), and AI model deployment and implementation (see section 'AI Clinical Trials'). We are interested in all forms of cardiovascular data, including ECG, MRI, radiological imaging, and particicularly interested in novel insights from pre-existing data. Our work has been published in Nature, Nature Medicine, NEJM AI, Circulation, JAMA Cardiology, EHJ, and other scientific venues. Our group works on multi-modal datasets, leveraging EHR, ECG, echo, and MRI data for a broad perspective on cardiovascular disease. Our lab members bring expertise from a wide range of backgrounds ranging from physics, mechanical engineering, computer science to cardiology, anesthesia, and internal medicine.

MRI is increasingly being used to detect prostate cancer, yet its interpretation can be challenging due to subtle differences between benign and cancerous regions. Many deep learning models have been developed to automatically detect prostate cancer, yet these methods can be further improved by incorporating useful anatomical information about prostate tumors (e.g. ~70% of prostate cancers occur in the peripheral zone). We quantified the heterogeneity of prostate cancer occurrence using a registration pipeline to compute voxel-level cancer distribution, and incorporated it as a prior distribution for a Denoising Diffusion Probabilistic Model (DDPM). Our method achieved statistically significantly higher accuracy and Dice coefficient than alternative models. As many AI methods have been developed for similar problems, a selective feature ensemble method is needed to combine their strength and eliminate incorrect predictions. We developed a multi-teacher distillation pipeline with a reinforcement-learning agent to dynamically determine distillation weights, thereby enriching the student model with useful knowledge and outperforming all teacher models.

The rigorous evaluation and stress-testing of clinical AI models is frequently constrained by a lack of diverse, precisely controlled data, especially for rare pathologies or subtle clinical edge cases. This talk presents a framework for generating high-fidelity, controllable 3D CT volumes by conditioning latent diffusion models on clinical text, anatomical segmentations, and demographics to create targeted evaluation datasets. We demonstrate this approach across two distinct domains: generating 3D chest CTs conditioned on radiology reports to simulate specific findings like pulmonary embolism and medical devices, and creating counterfactual head CTs to model varied manifestations of intracranial hemorrhage. By evaluating these synthetic volumes through downstream diagnostic tasks and sensitivity analyses, we show that synthetic data can effectively preserve clinically relevant signals and provide a path for the rigorous evaluation and stress-testing of AI models before clinical deployment.

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