Hear talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and healthcare. View more CV events here.

Healthcare is one of the highest-impact domains for AI, yet reliable deployment at scale remains difficult. To truly improve patient care and clinical workflows, AI must operate under real clinical constraints, not just in ideal lab settings. In practice, deployment is limited by high compute and memory costs, scarce labeled data, and distribution shifts across sites and time. Many clinically important findings are also rare and long-tailed, which makes generalization especially challenging. My research makes deployability a design objective by developing methods that stay accurate under strict resource and data constraints. In this talk, I will first discuss high-performance lightweight deep learning architectures built by redesigning core building blocks. I will then present training-time generative supervision strategies that improve data efficiency and generalization to rare and long-tailed cases with no inference overhead. I will conclude with a forward-looking direction toward real-time perception for surgical assistance, where reliable performance under strict constraints is non-negotiable.

VIGIL (Vectors of Intelligent Guidance in Long-Reach Rural Healthcare) is an AI-driven system designed to support generalist clinicians through interactive, multimodal guidance. The system combines perception, language understanding, and tool use to assist with tasks such as ultrasound acquisition and interpretation in real time. In this talk, we focus on the overall system architecture, highlighting how individual components—ranging from visual models to medical reasoning agents—interact to produce coherent guidance. We also discuss key challenges we have encountered, including tool orchestration, latency, and robustness across components. This presentation aims to provide a systems-level perspective on building embodied AI agents for real-world healthcare settings.

The scarcity of labeled, privacy-compliant medical imaging data remains one of the biggest bottlenecks in healthcare AI development. Emerging world models are changing this landscape by generating high-fidelity synthetic data — from radiology scans to surgical scene simulations — that can augment real-world datasets without compromising patient privacy. However, synthetic data is only as valuable as your ability to curate, validate, and evaluate it alongside real clinical data. In this talk, we explore how teams are using FiftyOne to build rigorous quality pipelines around synthetic medical imagery, enabling them to detect distribution gaps, measure model performance across rare pathologies, and ensure that generated samples meaningfully improve downstream diagnostics. We'll walk through practical workflows that combine world model outputs with real-world medical datasets to accelerate Visual AI in healthcare — responsibly and at scale.