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

Abstract

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. 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