Physicians and artificial intelligence diverge in evaluating large language models on real clinical cases

While multimodal large language models (LLMs) demonstrate significant potential in healthcare applications, their clinical utility is difficult to appraise. Current evaluations of medical-assisting LLMs are often limited by sparse human expertise, narrow specialty scope, and reliance on multiple-choice benchmarks or synthetic vignettes, which can inflate performance and obscure clinical utility. We conducted a multicenter, multidisciplinary study in which more than 400 physicians-spanning seven specialties, varied experience levels, and multiple geographic settings-evaluated LLM-generated free-text responses to real, de-identified clinical cases. In a matched-control design, we also deployed an equivalent number of AI agents configured to mirror physician characteristics to examine whether automated evaluators can supplement or replace human assessment. Our results demonstrated that physician assessments exhibited substantial heterogeneity by clinical seniority and practice environment, leading to notable shifts in relative model rankings across cohorts. While AI agents delivered highly efficient, directionally aligned assessments, they did not fully capture the nuances of human clinical judgment and could not substitute for physician-centered evaluation. Instead, they promise assistive tools that can triage or pre-screen outputs to reduce human burden.