Concordance of large language models with a hamstring strain guideline: Implications for athlete rehabilitation and return-to-sport guidance

Corekci A. M., Devran S., Yakal S., Uyanik A. F., Metin G.

INTERNATIONAL JOURNAL OF SPORTS SCIENCE & COACHING, 2026 (SSCI, Scopus)

Özet

Hamstring strain injury is a common sports-related injury with substantial recurrence risk, and large language models are increasingly used as sources of health information by athletes, coaches, and clinicians. This study evaluated the concordance of three contemporary large language models with the 2022 hamstring strain injury clinical practice guideline. Fourteen guideline-derived clinical questions were submitted to ChatGPT-5.2, Gemini 3 Pro, and DeepSeek-3.2v using identical single-turn prompts on January 17, 2026. Forty-two anonymized responses were independently scored by three blinded clinicians using a predefined, study-specific 5-point Clinical Practice Guideline Concordance Scoring Rubric. Inter-rater reliability was good to excellent across models. Mean concordance scores were high for ChatGPT-5.2, Gemini 3 Pro, and DeepSeek-3.2v, with no statistically significant between-model difference. No responses were rated discordant or potentially harmful. Lower concordance was observed in early-phase management and standardized outcome-measure domains. Contemporary large language models showed high overall concordance with the hamstring strain injury guideline, but variability in nuanced rehabilitation and return-to-sport domains supports the need for clinician oversight when AI-generated advice is used in sports rehabilitation contexts.