Large language models (LLMs) are increasingly used in clinical settings, raising concerns about racial bias in both generated medical text and clinical reasoning. Existing studies have identified bias in medical LLMs, but many focus on single models and give less attention to mitigation. This study uses the EU AI Act as a governance lens to evaluate five widely used LLMs across two tasks, namely synthetic patient-case generation and differential diagnosis ranking. Using race-stratified epidemiological distributions in the United States and expert differential diagnosis lists as benchmarks, we apply structured prompt templates and a two-part evaluation design to examine implicit and explicit racial bias. All models deviated from observed racial distributions in the synthetic case generation task, with GPT-4.1 showing the smallest overall deviation. In the differential diagnosis task, DeepSeek V3 produced the strongest overall results across the reported metrics. When embedded in an agentic workflow, DeepSeek V3 showed an improvement of 0.0348 in mean p-value, 0.1166 in median p-value, and 0.0949 in mean difference relative to the standalone model, although improvement was not uniform across every metric. These findings support multi-metric bias evaluation for AI systems used in medical settings and suggest that retrieval-based agentic workflows may reduce some forms of explicit bias in benchmarked diagnostic tasks. Detailed prompt templates, experimental datasets, and code pipelines are available on our GitHub.

翻译：大语言模型（LLMs）在临床场景中的应用日益广泛，这引发了人们对生成医学文本及临床推理中种族偏见的担忧。现有研究已识别出医疗LLMs中的偏见，但多数研究聚焦于单一模型，对缓解措施的关注较少。本研究以《欧盟人工智能法案》作为治理框架，评估了五种广泛使用的LLMs在两个任务中的表现：合成患者病例生成与鉴别诊断排序。以美国按种族分层的流行病学分布及专家鉴别诊断列表为基准，我们采用结构化提示模板及两阶段评估设计来考察隐性与显性种族偏见。在合成病例生成任务中，所有模型均偏离了实际种族分布，其中GPT-4.1的整体偏差最小。在鉴别诊断任务中，DeepSeek V3在各项报告指标上取得了最优综合表现。当嵌入智能体工作流时，与独立模型相比，DeepSeek V3的平均p值提升了0.0348，中位p值提升了0.1166，平均差异提升了0.0949，但改善幅度在各指标上并不均匀。这些发现支持对医疗场景中使用的AI系统进行多指标偏见评估，并表明基于检索的智能体工作流可能减少基准诊断任务中某些形式的显性偏见。详细的提示模板、实验数据集及代码流程已发布于我们的GitHub仓库。