GPT for RCTs? Using AI to determine adherence to clinical trial reporting guidelines

Abstract

OBJECTIVES: Adherence 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 accurate a large language model generative artificial intelligence system (AI-LLM) was for determining reporting guideline compliance in a sample of sports medicine clinical trial reports. DESIGN: This study was an exploratory retrospective data analysis. OpenAI GPT-4 and Meta Llama 2 AI-LLM were evaluated for their ability to determine reporting guideline adherence in a sample of sports medicine and exercise science clinical trial reports. SETTING: Academic research institution. PARTICIPANTS: The study sample included 113 published sports medicine and exercise science clinical trial papers. For each paper, the GPT-4 Turbo and Llama 2 70B models were prompted to answer a series of nine reporting guideline questions about the text of the article. The GPT-4 Vision model was prompted to answer two additional reporting guideline questions about the participant flow diagram in a subset of articles. The dataset was randomly split (80/20) into a TRAIN and TEST dataset. Hyperparameter and fine-tuning were performed using the TRAIN dataset. The Llama 2 model was fine-tuned using the data from the GPT-4 Turbo analysis of the TRAIN dataset. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the F1-score, a measure of model performance on the TEST dataset. The secondary outcome was the model's classification accuracy (%). RESULTS: Across all questions about the article text, the GPT-4 Turbo AI-LLM demonstrated acceptable performance (F1-score=0.89, accuracy (95% CI) = 90% (85% to 94%)). Accuracy for all reporting guidelines was >80%. The Llama 2 model accuracy was initially poor (F1-score=0.63, accuracy (95% CI) = 64% (57% to 71%)) and improved with fine-tuning (F1-score=0.84, accuracy (95% CI) = 83% (77% to 88%)). The GPT-4 Vision model accurately identified all participant flow diagrams (accuracy (95% CI) = 100% (89% to 100%)) but was less accurate at identifying when details were missing from the flow diagram (accuracy (95% CI) = 57% (39% to 73%)). CONCLUSIONS: Both the GPT-4 and fine-tuned Llama 2 AI-LLMs showed promise as tools for assessing reporting guideline compliance. Next steps should include developing an efficient, open-source AI-LLM and exploring methods to improve model accuracy.

Ähnliche Arbeiten