ArticleFrontiers in endocrinology2026
Comparative assessment of large language models in diabetic foot infection management: alignment with IWGDF/IDSA guidelines.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A comparative evaluation of large language models in aligning with European Respiratory Society (ERS) guidelines for high-flow nasal cannula in acute respiratory failure.Journal of thoracic disease · 2026Article
- Patient-facing diabetic foot information from large language models: a domain- and source-balanced prompt framework for public-interface benchmarking.Frontiers in endocrinology · 2026Article
- From risk classification to clinical action: a prespecified paired pilot benchmark of public large language model interfaces for diabetes-related foot ulcer prevention.Frontiers in endocrinology · 2026Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To assess the clinical utility of artificial intelligence (AI) models (ChatGPT-4o, DeepSeek-R1, Grok-3 and Claude-3.7) in aligning with international guidelines for diabetic foot infection (DFI) management. Background: AI systems have demonstrated their potential application value in numerous fields. However, the specific effects of these technologies in the medical and health sector still require in-depth exploration. DFI is a relatively common and serious complication among diabetic patients, and the accurate transmission of relevant information is of great significance. Therefore, it is particularly important to evaluate whether artificial intelligence can serve as an effective clinical auxiliary tool. Methods: Responses from ChatGPT-4o, DeepSeek-R1, Grok-3 and Claude-3.7 were evaluated against DFI guidelines using four clinical dimensions (Accuracy, Overconclusiveness, Supplementary Value, and Completeness) using a 5-point Likert scale, and assessed for readability using Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL). Statistical analyses included ANOVA and Results: No significant differences were found across models for Accuracy and Overconclusiveness ( Conclusion: 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.
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Registered trials
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