@inproceedings{deng-etal-2026-multidx,
title = "{M}ulti{D}x: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning",
author = "Deng, Yimin and
Lin, Zhenxi and
Wang, Yejing and
Zhao, Guoshuai and
Jia, Pengyue and
Fu, Zichuan and
Xu, Derong and
Zheng, Yefeng and
Zhao, Xiangyu and
Zhu, Li and
Wu, Xian and
Qian, Xueming",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1646/",
pages = "32904--32921",
ISBN = "979-8-89176-395-1",
abstract = "Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While large language models have shown strong capabilities in commonsense reasoning, they still struggle with diagnostic reasoning due to limited domain knowledge. Existing approaches often rely on internal model knowledge or static knowledge bases, which are insufficient to support the knowledge demands of diagnostic reasoning. Moreover, these methods focus solely on the accuracy of final predictions, overlooking alignment with standard clinical reasoning trajectories. To this end, we propose MultiDx, a two-stage diagnostic reasoning framework that performs differential diagnosis by analyzing evidence collected from multiple knowledge sources. Specifically, it first generates suspected diagnoses and reasoning traces by leveraging knowledge from web search, SOAP-formatted case, and clinical case database. Then it integrates multi-perspective evidence through matching, voting, and differential diagnosis to generate the final prediction. Extensive experiments demonstrate the effectiveness of our approach."
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<abstract>Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While large language models have shown strong capabilities in commonsense reasoning, they still struggle with diagnostic reasoning due to limited domain knowledge. Existing approaches often rely on internal model knowledge or static knowledge bases, which are insufficient to support the knowledge demands of diagnostic reasoning. Moreover, these methods focus solely on the accuracy of final predictions, overlooking alignment with standard clinical reasoning trajectories. To this end, we propose MultiDx, a two-stage diagnostic reasoning framework that performs differential diagnosis by analyzing evidence collected from multiple knowledge sources. Specifically, it first generates suspected diagnoses and reasoning traces by leveraging knowledge from web search, SOAP-formatted case, and clinical case database. Then it integrates multi-perspective evidence through matching, voting, and differential diagnosis to generate the final prediction. Extensive experiments demonstrate the effectiveness of our approach.</abstract>
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%0 Conference Proceedings
%T MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning
%A Deng, Yimin
%A Lin, Zhenxi
%A Wang, Yejing
%A Zhao, Guoshuai
%A Jia, Pengyue
%A Fu, Zichuan
%A Xu, Derong
%A Zheng, Yefeng
%A Zhao, Xiangyu
%A Zhu, Li
%A Wu, Xian
%A Qian, Xueming
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F deng-etal-2026-multidx
%X Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While large language models have shown strong capabilities in commonsense reasoning, they still struggle with diagnostic reasoning due to limited domain knowledge. Existing approaches often rely on internal model knowledge or static knowledge bases, which are insufficient to support the knowledge demands of diagnostic reasoning. Moreover, these methods focus solely on the accuracy of final predictions, overlooking alignment with standard clinical reasoning trajectories. To this end, we propose MultiDx, a two-stage diagnostic reasoning framework that performs differential diagnosis by analyzing evidence collected from multiple knowledge sources. Specifically, it first generates suspected diagnoses and reasoning traces by leveraging knowledge from web search, SOAP-formatted case, and clinical case database. Then it integrates multi-perspective evidence through matching, voting, and differential diagnosis to generate the final prediction. Extensive experiments demonstrate the effectiveness of our approach.
%U https://aclanthology.org/2026.findings-acl.1646/
%P 32904-32921
Markdown (Informal)
[MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning](https://aclanthology.org/2026.findings-acl.1646/) (Deng et al., Findings 2026)
ACL
- Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, and Xueming Qian. 2026. MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 32904–32921, San Diego, California, United States. Association for Computational Linguistics.