让医学问答理解患者具体病情，避免误判
Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
- 构建带条件判断的知识图谱，只选符合患者情况的推理路径
- 在新基准上准确率超现有方法，关键路径选择更可靠
- 适合医疗AI研发、临床决策系统优化者阅读
当前生物医学问答系统常假设医学知识通用适用，但真实临床推理具有强条件性：几乎每个决策都依赖患者特异性因素，如合并症和禁忌症。现有评测基准未评估此类条件推理，检索增强或图结构方法也缺乏显式机制确保召回知识与上下文匹配。为此，我们提出CondMedQA，首个面向条件性生物医学问答的基准，包含多跳问题，答案随患者条件变化。同时提出条件门控推理（CGR）框架，构建条件感知知识图谱，并根据查询条件选择或剪枝推理路径。实验表明，CGR在保证或超越现有最优性能的同时，更可靠地选出适配条件的答案，凸显显式建模条件性对鲁棒医学推理的重要性。
原文摘要 · Abstract (English)
Current biomedical question answering (QA) systems often assume that medical knowledge applies uniformly, yet real-world clinical reasoning is inherently conditional: nearly every decision depends on patient-specific factors such as comorbidities and contraindications. Existing benchmarks do not evaluate such conditional reasoning, and retrieval-augmented or graph-based methods lack explicit mechanisms to ensure that retrieved knowledge is applicable to given context. To address this gap, we propose CondMedQA, the first benchmark for conditional biomedical QA, consisting of multi-hop questions whose answers vary with patient conditions. Furthermore, we propose Condition-Gated Reasoning (CGR), a novel framework that constructs condition-aware knowledge graphs and selectively activates or prunes reasoning paths based on query conditions. Our findings show that CGR more reliably selects condition-appropriate answers while matching or exceeding state-of-the-art performance on biomedical QA benchmarks, highlighting the importance of explicitly modeling conditionality for robust medical reasoning.
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