Clinician input steers AI toward accurate and harmful recommendations
Large language models (LLMs) are entering clinical workflows, yet evaluations rarely assess how clinician reasoning shapes model behavior during clinical interactions. Using 61 curated NEJM Case Records, we tested how expert or misleading clinician reasoning influenced AI-generated differential diagnoses and next step recommendations across 21 reasoning variants from 8 proprietary and open-source models. After clinician exposure, LLM-clinician concordance increased: simulations with >=3 overlapping differential diagnoses rose from 65.8% to 93.5%, and those with >=3 overlapping next step recommendations from 20.3% to 53.8%. Expert context significantly improved correct final-diagnosis incl...
| Authors | Ivan Lopez, Selin S. Everett, Bryan J. Bunning et al. |
|---|---|
| Primary category | cs.HC |
| Published | 14 March 2026 |
| Last updated at source | 6 August 2026 |
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