Disagreement between human and AI evaluation of treatment plans

Large language models (LLMs) are increasingly advocated for clinical decision support, yet it remains unclear how their recommendations are evaluated relative to human-authored plans when presentation cues are controlled. We conducted a vignette-based study in dermatology in which experienced clinicians (n = 10) and two LLMs (a generalist model and a deliberative reasoning model) drafted treatment plans for five de-identified cases. All plans were normalized for structure, length and tone before being blindly scored on a rubric by the same clinician cohort and by an AI judge. The primary outcome was the difference in plan scores as a function of evaluator identity (human vs. AI). We observed a consistent evaluator effect: clinician raters tended to assign higher scores to clinician-authored plans, whereas the AI judge tended to assign higher scores to AI-authored plans. Because plans were standardized and authorship was masked, this divergence is unlikely to be explained solely by surface presentation, suggesting that humans and AI apply partly different internal criteria when judging plan quality. The study is exploratory and limited by the small sample, synthetic vignettes and the use of a single AI judge; scores reflect perceived quality rather than patient outcomes. These findings indicate that evaluator identity systematically shapes judgments of clinical plans under controlled conditions and motivate multi-metric, context-aware evaluation frameworks that capture the multidimensional nature of clinical reasoning, along with human–AI interfaces that make assumptions and criteria explicit.