71% of FDA-Approved AI May Be a Clinical Liability (Here’s Why)

When 71% of training data for FDA-approved AI comes from just three states, geographic bias shifts from a technical oversight to a direct clinical liability. To deliver on the promise of value-based care, we must move beyond "clean" academic datasets and integrate the diverse, real-world data that reflects the actual patients we serve.

In this episode, Dr. Junaid Kalia and Dr. Harvey Castro sit down with Dr. Martin Willemink, a radiologist and former Stanford faculty member who transitioned into the entrepreneurial world to solve AI’s most significant bottleneck: data diversity. As a clinician-turned-founder of Segmed, Dr. Willemink provides a systematic look at why "garbage in, garbage out" is a systemic risk to patient safety and how building a more representative data pipeline is the only way to achieve true generalizability in healthcare AI.