Most of us working at the intersection of clinical medicine and AI have seen firsthand the incredible progress we’ve made with pattern recognition models. From radiology to pathology, deep learning has proven remarkably good at identifying anomalies, segmenting images, and predicting risk. But as we push AI deeper into the clinical workflow—beyond identifying features and toward making sense of them—we’re hitting a wall.
That wall is reasoning.Clinicians don’t just see a nodule and call it cancer. We weigh probabilities. We consider the patient’s history, their labs, their symptoms, the meds they’re on, the things they’re not saying. We reason. And if we want clinical AI to play a meaningful role in diagnosis or treatment planning—not just in back-office automation—we need models that can do more than detect. We need models that can think.Explore the Full Story…
