Medicine is inherently pluralistic. Principles such as autonomy, beneficence, nonmaleficence, and justice routinely conflict, and such ethical dilemmas often sharply divide reasonable physicians. Good clinical practice navigates these tensions in concert with each patient's values rather than imposing a single ethical stance. The ethical values that large language models bring to medical advice, however, have not been systematically examined. We present a framework for auditing value pluralism in medical AI, comprising a benchmark of clinician-verified dilemmas and an attribution method that recovers value priorities directly from decisions. The ecosystem of frontier models spans physician-level value heterogeneity, and models discuss competing values in their reasoning (Overton pluralism) before committing to a decision. However, individual model decisions are near-deterministic across repeated sampling and semantic variations, failing to reproduce the distributional pluralism of the physician panel. Across benchmark cases, these consistent decisions reflect committed, systematic value preferences. While most model priorities fall within the natural range of inter-physician variation, some significantly underweight patient autonomy. A single LLM deployed without regard for its value priorities could amplify those priorities at scale to every patient it serves. Without explicit efforts to balance ethical perspectives with one or multiple models, these tools risk replacing clinical pluralism with a deployment monoculture.
AI医生看重什么？——语言模型临床伦理中的多元性审计 / What Does the AI Doctor Value? Auditing Pluralism in the Clinical Ethics of Language Models
该研究设计了一套审计框架，用于评估大型语言模型在医疗建议中隐含的伦理价值偏好，发现虽然不同模型整体上覆盖了医生群体的价值多样性，但单个模型的决策几乎固定不变，且部分模型显著轻视患者自主权，若不加以干预，大规模部署可能导致临床伦理从多元走向单一。
源自 arXiv: 2605.18738
