In this episode of Practical AI in Healthcare, we sit down with Dr. Jeff Chuang, a computational biologist at The Jackson Laboratory, to explore how AI is reshaping cancer diagnostics, starting with pediatric sarcoma. Jeff shares his journey from physics and protein folding to computational pathology, where machine learning is being applied to standard H&E pathology slides to deliver faster, cheaper, and more accurate diagnoses.

The conversation dives into how AI models trained on relatively small but carefully curated image datasets can outperform traditional diagnostic approaches, especially in rare cancers where expertise is scarce. We also explore the challenges of data sharing, IRB approvals, and real-world deployment, along with a glimpse into the future of spatial genomics and ultra-high-resolution tissue analysis. This episode is a powerful example of how practical AI can directly improve patient care today.