@inproceedings{niu-etal-2026-med,
title = "{M}ed-Chain: An Adaptive Clinical-Chain Retrieval Framework for Evidence-Grounded Medical Question Answering",
author = "Niu, Fucheng and
Jiang, Songtao and
Wu, Qinxin and
Shan, Sylvia Chung Yan and
Hu, Tianxiang and
Wu, Jian and
Liu, Zuozhu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1110/",
pages = "23489--23508",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Models (LLMs) have shown strong potential in medical question answering, but their reliance on static parametric knowledge limits their ability to handle rapidly evolving clinical evidence. The retrieval system mitigates this issue by incorporating external medical knowledge, but flat retrieval can return lexically relevant but clinically misaligned evidence. We propose Med-Chain, a training-free medical evidence retrieval framework that reformulates retrieval as inspectable and repairable clinical evidence paths. Med-Chain first routes each question through a clinical topic tree to progressively narrow retrieval from broad domains to specific guideline sections. It then verifies whether the retrieved path matches the clinical intent and performs level-aware refinement to repair mismatches. Experiments on seven medical QA benchmarks show that Med-Chain consistently improves backbone LLMs and outperforms existing medical Retrieval-augmented generation baselines, highlighting the value of clinically structured and repairable evidence retrieval."
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<abstract>Large Language Models (LLMs) have shown strong potential in medical question answering, but their reliance on static parametric knowledge limits their ability to handle rapidly evolving clinical evidence. The retrieval system mitigates this issue by incorporating external medical knowledge, but flat retrieval can return lexically relevant but clinically misaligned evidence. We propose Med-Chain, a training-free medical evidence retrieval framework that reformulates retrieval as inspectable and repairable clinical evidence paths. Med-Chain first routes each question through a clinical topic tree to progressively narrow retrieval from broad domains to specific guideline sections. It then verifies whether the retrieved path matches the clinical intent and performs level-aware refinement to repair mismatches. Experiments on seven medical QA benchmarks show that Med-Chain consistently improves backbone LLMs and outperforms existing medical Retrieval-augmented generation baselines, highlighting the value of clinically structured and repairable evidence retrieval.</abstract>
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%0 Conference Proceedings
%T Med-Chain: An Adaptive Clinical-Chain Retrieval Framework for Evidence-Grounded Medical Question Answering
%A Niu, Fucheng
%A Jiang, Songtao
%A Wu, Qinxin
%A Shan, Sylvia Chung Yan
%A Hu, Tianxiang
%A Wu, Jian
%A Liu, Zuozhu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F niu-etal-2026-med
%X Large Language Models (LLMs) have shown strong potential in medical question answering, but their reliance on static parametric knowledge limits their ability to handle rapidly evolving clinical evidence. The retrieval system mitigates this issue by incorporating external medical knowledge, but flat retrieval can return lexically relevant but clinically misaligned evidence. We propose Med-Chain, a training-free medical evidence retrieval framework that reformulates retrieval as inspectable and repairable clinical evidence paths. Med-Chain first routes each question through a clinical topic tree to progressively narrow retrieval from broad domains to specific guideline sections. It then verifies whether the retrieved path matches the clinical intent and performs level-aware refinement to repair mismatches. Experiments on seven medical QA benchmarks show that Med-Chain consistently improves backbone LLMs and outperforms existing medical Retrieval-augmented generation baselines, highlighting the value of clinically structured and repairable evidence retrieval.
%U https://aclanthology.org/2026.findings-emnlp.1110/
%P 23489-23508
Markdown (Informal)
[Med-Chain: An Adaptive Clinical-Chain Retrieval Framework for Evidence-Grounded Medical Question Answering](https://aclanthology.org/2026.findings-emnlp.1110/) (Niu et al., Findings 2026)
ACL