AI-Driven Medical Device Risk Management: A New Paradigm Integrating Large Language Models and Prompt Engineering for Standard-Risk Knowledge Graph Construction and Application
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.
Authors
- Wenke Xia (ORCID: https://orcid.org/0009-0000-1597-9512)
- W. Li
- Peiming Zhang
- Ziming Gao (ORCID: https://orcid.org/0009-0001-5383-9315)
- Ruixue Tian
- Wanting Zhu
- Li Wang
Institutions
- University of Shanghai for Science and Technology (CN)
- China Banking Regulatory Commission (CN)
- Shanghai Medical Information Center (CN)
Publication Details
- Journal
- Risk Management and Healthcare Policy
- Published
- 2026-01-01
- DOI
- https://doi.org/10.2147/rmhp.s571156
- Primary Topic
- Advanced Graph Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Shanghai Education Development Foundation
- Shanghai Municipal Education Commission