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AI-Driven Medical Device Risk Management: A New Paradigm Integrating Large Language Models and Prompt Engineering for

Wanting Zhu1, Peiming Zhang1, Wenke Xia1

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Educational Institution, Shanghai, People's Republic of China.

Risk Management and Healthcare Policy
|March 20, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered method to build risk knowledge graphs for medical electrical equipment, improving adverse event analysis and safety management.

Keywords:
intelligent risk supervisionknowledge graphlarge language modelmedical electrical equipment standards documentsprompt engineering

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Area of Science:

  • Medical device safety
  • Artificial intelligence in healthcare
  • Knowledge representation

Background:

  • Current medical electrical equipment risk management faces challenges due to fragmented standards and data.
  • Lack of high-quality annotated data hinders effective manual risk analysis.

Purpose of the Study:

  • To develop a novel method for constructing a risk knowledge graph.
  • To integrate unstructured medical electrical equipment standards with adverse event data.
  • To enhance risk analysis and management in the medical device field.

Main Methods:

  • Integration of large language models (LLMs) and prompting engineering.
  • Construction of a three-layer risk knowledge system using multi-source standards.
  • Development of multi-angle prompting strategies and entity disambiguation/aggregation for knowledge integration.

Main Results:

  • Achieved a mean F1 score of 0.871 with thought chain reasoning suggestion.
  • Constructed a knowledge graph with 24,106 nodes and 18,053 relationships.
  • Established a complete 'fault-standard-measure' link and developed an intelligent risk retrieval system.

Conclusions:

  • Presents a low-cost, reusable knowledge graph construction approach for resource-constrained medical device sectors.
  • Promotes AI-driven transformation in medical device risk management.
  • Aids in intelligent supervision of medical device adverse events.