ArticleJournal of orthopaedic surgery and research2025
Assessment of ChatGPT's adherence to evidence-based clinical practice guidelines for plantar fasciitis management.
Article in Journal of orthopaedic surgery and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of natural language processing and large language models in sports injury assessment and rehabilitation decision-making: a scoping review.Frontiers in medicine · 2026Pooled it
- Generative AI and Large Language Models in Rehabilitation: A Scoping Review.Life (Basel, Switzerland) · 2026Review
- Performance of DeepSeek-R1 and ChatGPT-5 in the Generation of North American Spine Society Clinical Guidelines for Adult Vertebral Compression Fractures: Comparative Study.Journal of medical Internet research · 2026Article
- Comparative performance of ChatGPT and DeepSeek in interpreting the 2025 ESICM guidelines on sepsis fluid therapy.Digital healthArticle
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to test the multidimensional performance of Chat-Generative Pre-trained Transformer (ChatGPT) in generating recommendations for the management of plantar fasciitis (PF) that adhere to well-established clinical practice guidelines. MATERIALS AND
methods21 queries were raised from the 2023 APTA guideline recommendations for PF and prompted into ChatGPT-4o and ChatGPT-4 Turbo. Two experienced orthopaedic physicians evaluated the responses for accuracy, consistency, self-awareness, and fabrication and falsification using five-point Likert scales. The group-wise comparisons were conducted between the two models and subgroups.
resultsThe interrater agreement between evaluators was moderate to good (intraclass correlation coefficients of 0.573-0.757). Both versions of ChatGPT were outperformed and comparable across all dimensions, including accuracy ([4.1 ± 0.8] vs. [4.1 ± 0.7], P = 0.959), consistency ([4.6 ± 0.5] vs. [4.6 ± 0.6], P = 0.890), self-awareness ([4.3 ± 0.6] vs. [4.5 ± 0.5], P = 0.407), and fabrication and falsification ([4.6 ± 0.6] vs. [4.5 ± 0.4], P = 0.681). In the subgroup comparisons, better performance was identified in closed-ended questions and for positive rather than negative recommendations (P < 0.05). No significant differences were found between recommendation strength subgroups, except in fabrication and falsification ([4.4 ± 0.6] vs. [5.0 ± 0], P = 0.001).
conclusionsThe two mainstream versions of ChatGPT showed comparable and superior performance in generating recommendations concordant with clinical guidelines for PF management. However, notable specific issues included performance variations between different prompt strategies, recommendation grades, and recommendation type, and the models should still be utilized with caution.
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