AI-Driven Medical Device Risk Management: A New Paradigm Integrating Large Language Models and Prompt Engineering for Standard-Risk Knowledge Graph Construction and Application

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 ChinaCorrespondence: 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 [email protected]: 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