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已发表论文
AI 驱动的医疗器械风险管理：整合大型语言模型和提示工程用于标准风险知识图谱构建与应用的新范式
Authors Zhu W , Zhang P, Xia W, Gao Z , Li W, Tian R, Wang L
Received 30 September 2025
Accepted for publication 23 December 2025
Published 9 January 2026 Volume 2026:19 571156
DOI https://doi.org/10.2147/RMHP.S571156
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Keon-Hyung Lee
Wanting Zhu,1 Peiming Zhang,1 Wenke Xia,1 Ziming Gao,2 Weiqi Li,1 Ruixue Tian,3 Li Wang4
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Educational Institution, Shanghai, People’s Republic of China; 2Oriental Pan-Vascular Devices Innovation College, University of Shanghai for Science and Technology, Educational Institution, Shanghai, People’s Republic of China; 3Lin-Gang Medical Device Innovation Center, Other Institution, Shanghai, People’s Republic of China; 4Henan Drug Evaluation Center, Regulatory Institution, Zhengzhou, People’s Republic of China
Correspondence: Peiming Zhang, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516, Jungong Road, Yangpu District, Shanghai, People’s Republic of China, Email zpmking@163.com
Purpose: To address the problems in medical electrical equipment risk management caused by the disconnection between unstructured medical electrical equipment standard documents and adverse event data, the lack of high-quality annotated data, and the reliance on manual combing for risk analysis.
Methods: This paper proposes a novel method for constructing a risk knowledge graph that integrates large language models and prompting engineering standards. Using adverse event data from early childhood incubators as a case study, it integrates multi-source standards to construct a three-layer risk knowledge system. It designs multi-angle prompting strategies involving entity relationships and employs a dual strategy of entity disambiguation and aggregation to achieve knowledge integration and standardization.
Results: The thought chain reasoning suggestion has the best performance (mean F1 score of 0.871). The constructed knowledge graph contains 24,106 nodes and 18,053 relationships, achieving a complete “fault-standard-measure” link. Based on this, a question-answering system for intelligent risk retrieval was developed.
Conclusion: This provides a low-cost, reusable knowledge graph construction path for the resource-constrained medical device field, promoting the transformation of risk management towards AI empowerment and assisting in intelligent supervision of adverse events related to medical devices.
Keywords: knowledge graph, large language model, prompt engineering, medical electrical equipment standards documents, intelligent risk supervision
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Educational Institution, Shanghai, People’s Republic of China; 2Oriental Pan-Vascular Devices Innovation College, University of Shanghai for Science and Technology, Educational Institution, Shanghai, People’s Republic of China; 3Lin-Gang Medical Device Innovation Center, Other Institution, Shanghai, People’s Republic of China; 4Henan Drug Evaluation Center, Regulatory Institution, Zhengzhou, People’s Republic of China
Correspondence: Peiming Zhang, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516, Jungong Road, Yangpu District, Shanghai, People’s Republic of China, Email zpmking@163.com
Purpose: To address the problems in medical electrical equipment risk management caused by the disconnection between unstructured medical electrical equipment standard documents and adverse event data, the lack of high-quality annotated data, and the reliance on manual combing for risk analysis.
Methods: This paper proposes a novel method for constructing a risk knowledge graph that integrates large language models and prompting engineering standards. Using adverse event data from early childhood incubators as a case study, it integrates multi-source standards to construct a three-layer risk knowledge system. It designs multi-angle prompting strategies involving entity relationships and employs a dual strategy of entity disambiguation and aggregation to achieve knowledge integration and standardization.
Results: The thought chain reasoning suggestion has the best performance (mean F1 score of 0.871). The constructed knowledge graph contains 24,106 nodes and 18,053 relationships, achieving a complete “fault-standard-measure” link. Based on this, a question-answering system for intelligent risk retrieval was developed.
Conclusion: This provides a low-cost, reusable knowledge graph construction path for the resource-constrained medical device field, promoting the transformation of risk management towards AI empowerment and assisting in intelligent supervision of adverse events related to medical devices.
Keywords: knowledge graph, large language model, prompt engineering, medical electrical equipment standards documents, intelligent risk supervision