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.

翻译：当前的生物医学问答系统通常假设医学知识具有普适性，然而真实世界的临床推理本质上是条件性的：几乎每个医疗决策都取决于患者特异性因素，如合并症与禁忌症。现有基准测试未能评估此类条件推理，而检索增强或基于图的方法缺乏显式机制来确保检索到的知识适用于给定情境。为填补这一空白，我们提出了CondMedQA——首个面向条件性生物医学问答的基准测试，该数据集包含答案随患者状况变化的多跳问题。此外，我们提出了条件门控推理框架，该创新框架通过构建条件感知知识图谱，并基于查询条件选择性激活或剪枝推理路径。实验结果表明，CGR在保持或超越生物医学问答基准测试最先进性能的同时，能更可靠地选择符合特定条件的答案，这凸显了显式建模条件性对于实现稳健医学推理的重要性。