BackgroundAdherence to established reporting guidelines can improve clinical trial reporting standards, but attempts to improve adherence have produced mixed results. This exploratory study aimed to determine how accurately a Large Language Model generative AI system (AI-LLM) could measure reporting guideline compliance in a sample of sports medicine clinical trial reports.MethodsThe OpenAI GPT-3.5 AI-LLM was evaluated for its ability to determine reporting guideline adherence in a sample of 113 published sports medicine and exercise science clinical trial reports. For each paper, the model was prompted to answer a series of nine reporting guideline questions. The dataset was randomly split (80/20) into a TRAIN and TEST dataset. Hyperparameter and model fine-tuning were performed using the TRAIN dataset. Model performance (F1-score, classification accuracy) was assessed using the TEST dataset.ResultsAcross all questions, the AI-LLM demonstrated acceptable performance (F1-score = 86%). However, there was significant variation in performance between different reporting guideline questions (accuracy between 70-100%). The model was most accurate when asked to identify a defined primary objective or endpoint and least accurate when asked to identify an effect size and related confidence interval.DiscussionThe AI-LLM showed promise as a tool for assessing reporting guideline compliance. Next steps should include developing a cost-effective, open-source AI-LLM and exploring methods to improve model accuracy.