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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 10

Retrieval-augmented generation with prompt-guided multi-hop reasoning for medical diagnosis

Qin Le1
Gou Zhinan1,3
Wang Peiwu2
Zhang Gaofei2
Liu Siyu1
Gao Kai2
1. School of Management Science & Information Engineering, Hebei University of Economics & Business, Shijiazhuang 050061, China
2. School of Information Science & Engineering, Hebei University of Science & Technology, Shijiazhuang 050018, China
3. Dept. of Computer Science & Technology, Tsinghua University, Beijing 100084, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0089

Abstract

The complexity of medical diagnosis tasks is particularly prominent in the manifestations of symptoms and their associations with diseases. Due to the complexities of "different symptoms for the same disease" and "same symptoms for different diseases", medical diagnosis tasks impose higher requirements on the reasoning capabilities of models. Traditional Retrieval-augmented Generation (RAG) technologies, with their static retrieval and single-step reasoning, struggle to capture multi-level logical relationships (such as symptoms → departments → diseases → differential diagnosis) . To effectively overcome this limitation, this study proposes a novel framework for the medical field: a Prompt-Guided Multi-Hop Reasoning Retrieval-augmented Generation model for medical diagnosis (PGM-RAG) . By integrating basic knowledge of the medical field, this framework provides clear reasoning guidance for the model through the design of prompt information for each reasoning step. Meanwhile, this framework designs a quantitative rewriting mechanism around strictly control the accuracy of the content generated by large language models, thereby enhancing the reliability of the reasoning process and the precision of diagnostic results. Experiments on two public medical datasets, Huatuo-26M and WebMedQA, show that the model in this paper outperforms the existing best methods by 12.6% and 8.9% in EM and F1 metrics, respectively. Ablation experiments demonstrate that the multi-hop reasoning chain and the quantitative rewriting mechanism significantly improve the model's performance.

Foundation Support

河北省自然科学基金资助项目(F2023207003)
河北省高等教育教学改革研究与实践项目(2023GJJG187)
河北经贸大学教学研究项目(2024JYQ09)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0089
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 10

Publish History

[2025-06-18] Accepted Paper

Cite This Article

秦乐, 勾智楠, 王培伍, 等. 基于提示引导多跳推理的医学诊断检索增强生成 [J]. 计算机应用研究, 2025, 42 (10). (2025-06-19). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0089. (Qin Le, Gou Zhinan, Wang Peiwu, et al. Retrieval-augmented generation with prompt-guided multi-hop reasoning for medical diagnosis [J]. Application Research of Computers, 2025, 42 (10). (2025-06-19). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0089. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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