Comparative assessment of large language models in diabetic foot infection management: alignment with IWGDF/IDSA guidelines
All models perform comparably in terms of accuracy and in avoiding over-conclusions. Grok-3 outperformed the other models in the dimensions of complementarity and completeness. DeepSeek-R1 generated the most complex text. These findings validate the feasibility of AI in the standardized management of DFI, but the models still need to be further verified through clinical trials to determine their value in the real-world decision-making process.
Authors
- Mingna Fan
- Haifang Zhou (ORCID: https://orcid.org/0000-0001-7774-5623)
- Hongxia Wu (ORCID: https://orcid.org/0000-0001-7209-2109)
- Liyang Chang
- Lumeng Lu (ORCID: https://orcid.org/0009-0004-1555-7095)
- Chuanbo Liu (ORCID: https://orcid.org/0009-0009-1636-5614)
- Danni Yu (ORCID: https://orcid.org/0000-0002-1868-109X)
- Kai Wang (ORCID: https://orcid.org/0009-0002-2120-5788)
- Hanbin Wang (ORCID: https://orcid.org/0000-0002-9607-8114)
- Li Xu (ORCID: https://orcid.org/0000-0003-3255-326X)
- Xu Qiu
- Y. Wang
- Jiayi Deng
- Zhaohuan Chen
Institutions
- Zhejiang Chinese Medical University (CN)
- Westlake University (CN)
- Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2026-02-24
- DOI
- https://doi.org/10.3389/fendo.2026.1667159
- Primary Topic
- Diabetic Foot Ulcer Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00