构建首个临床诊断推理基准，评估模型是否真能基于正确证据做判断。
MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams
- 基于西班牙住院医师考试案例，标注证据片段与论点关系
- 三阶段评测：找证据、抽论点、判支持/攻击关系，覆盖1024个病例
- 首次提供巴斯克语版本，助力多语言医学AI发展
临床自然语言处理评估仍以多选题为主，仅衡量最终答案正确性，无法判断模型是否基于相关、缺失或矛盾的证据得出正确诊断。我们提出MIRA-Ev，一个基于西班牙医学住院医师考试（MIR）案例的临床论证挖掘基准，由专家医生重新标注了句子级前提、主张及有向支持/攻击关系，并同步发布西班牙语（母语）、英语和巴斯克语版本，是首个巴斯克语临床论证资源。MIRA-Ev采用三级任务体系：证据句检索、论证成分抽取与关系分类，推动更细致的临床推理评估。
原文摘要 · Abstract (English)
Clinical NLP evaluation remains dominated by multiple-choice question answering (MCQA), which scores only final-answer accuracy and cannot detect when a model reaches the correct diagnosis while grounding it in irrelevant, absent, or contradictory evidence. We introduce MIRA-Ev, a clinical argument mining benchmark built on Spanish Médico Interno Residente (MIR) licensing-exam cases, re-annotated by expert clinicians with span-level premises, claims, and directed support/attack relations, and released in parallel Spanish (native), English, and Basque versions, the first clinical argumentation resource in Basque. MIRA-Ev organizes evaluation into a three-tier task hierarchy: evidence sentence retrieval, argumentative component extraction, and relation classification.
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