While large language models (LLMs) hold promise for transforming clinical healthcare, current comparisons and benchmark evaluations of large language models in medicine often fail to capture real-world efficacy. Specifically, we highlight how key discrepancies arising from choices of data, tasks, and metrics can limit meaningful assessment of translational impact and cause misleading conclusions. Therefore, we advocate for rigorous, context-aware evaluations and experimental transparency across both research and deployment.
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This study investigates how a physician’s use of generative AI (GenAI) in medical decision‑making is perceived by peer clinicians. In a randomized experiment, 276 practicing clinicians evaluated one of three vignettes depicting a physician: (1) using no GenAI (Control), (2) using GenAI as a primary decision-making tool (GenAI-primary), and (3) using GenAI as a verification tool (GenAI-verify). Participants rated the physician depicted in the GenAI‑primary condition significantly lower in clinical skill (on a 1–7 scale; mean = 3.79) than in the Control condition (5.93, p < 0.001). Framing GenAI use as verification partially mitigated this effect (4.99, p < 0.001). Similar patterns appeared for perceived overall healthcare experience and competence. Participants also acknowledged GenAI’s value in improving accuracy (4.30, p < 0.002) and rated institutionally customized GenAI more favorably (4.96, p < 0.001). These findings suggest that while clinicians see GenAI as helpful, its use can negatively impact peer evaluations. These effects can be reduced, but not fully eliminated, by framing it as a verification aid.
Scott McGrath
Reddit Tech VN Bot