AI handles patterns with ease.

Humans feel the weight of being wrong.

That’s where the divide becomes clear and important.

A recent study from the Icahn School of Medicine at Mount Sinai evaluated a health-focused version of ChatGPT using 60 clinician-authored patient scenarios. The findings were important and cast a light on the utility of large language models in medicine. Here's the key takeaway: In 52% of cases that physicians unanimously judged to require emergency care, ChatGPT did not recommend it. It performed well in routine complaints and in textbook emergencies where the pattern was clear. But it stumbled in the gray zone, where clinical signs were subtle and the cost of being wrong carried real consequence.

Consider a patient with abdominal pain or fever of unknown origin. The presentation may not scream catastrophe. The vital signs are not yet extreme and the laboratory data are incomplete. The experienced clinician senses trajectory, not just snapshot. That sense often leads to escalation before certainty arrives.

The inverted U curve revealed by the study is more than a product critique. It's my sense that this is a cognitive signature of AI and certainly worth a closer look.

The Architecture Reveals Itself

Lets start there. The system did not fail because it was reckless; it behaved according to its design. LLMs are engines of computation. They aggregate patterns across vast data sets and generate responses that are statistically coherent........

© Psychology Today