Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
This work addresses the need for robust conditional reasoning in biomedical QA for clinical applications, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the problem of biomedical question answering by addressing the lack of conditional reasoning in existing systems, proposing CondMedQA as a new benchmark and Condition-Gated Reasoning (CGR) as a framework, which matches or exceeds state-of-the-art performance while improving reliability in selecting condition-appropriate answers.
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.