The accuracy-safety trade-off of large reasoning models in clinical diagnostics nature.com

A study led by Shuai Wu, Linwei Chen and colleagues found that large reasoning models made more accurate primary diagnoses but produced 34% more false positives and were about 40% less reproducible than paired standard language models on a benchmark of electronic health records across 12 specialties. In a 15-physician study, clinician review of the models’ suggestions raised diagnostic accuracy by 20% and cut false positives by 44%; the authors say prospective testing is needed before clinical use.