Abstract
INTRODUCTION: The rapid advancement of artificial intelligence (AI), particularly the development of Large Language Models (LLMs) such as Generative Pretrained Transformers (GPTs), has revolutionized numerous fields. The purpose of this study is to investigate the application of LLMs within the realm of orthopaedic in training examinations. METHODS: Questions from the 2020-2022 Orthopaedic In-Service Training Exams (OITEs) were given to OpenAI's GPT-3.5 Turbo and GPT-4 LLMs, using a zero-shot inference approach. Each model was given a multiple-choice question, without prior exposure to similar queries, and their generated responses were compared to the correct answer within each OITE. The models were evaluated on overall accuracy, performance on questions with and without media, and performance on first- and higher-order questions. RESULTS: The GPT-4 model outperformed the GPT-3.5 Turbo model across all years and question categories (2022: 67.63% vs. 50.24%; 2021: 58.69% vs. 47.42%; 2020: 59.53% vs. 46.51%). Both models showcased better performance with questions devoid of associated media, with GPT-4 attaining accuracies of 68.80%, 65.14%, and 68.22% for 2022, 2021, and 2020, respectively. GPT-4 outscored GPT-3.5 Turbo on first-order questions across all years (2022: 63.83% vs. 38.30%; 2021: 57.45% vs. 50.00%; 2020: 65.74% vs. 53.70%). GPT-4 also outscored GPT-3.5 Turbo on higher-order questions across all years (2022: 68.75% vs. 53.75%; 2021: 59.66% vs. 45.38%; 2020: 53.27% vs. 39.25%). DISCUSSION: GPT-4 showed improved performance compared to GPT-3.5 Turbo in all tested categories. The results reflect the potential and limitations of AI in orthopaedics. GPT-4's performance is comparable to a second-to-third-year resident and GPT-3.5 Turbo's performance is comparable to a first-year resident, suggesting the application of current LLMs can neither pass the OITE nor substitute orthopaedic training. This study sets a precedent for future endeavors integrating GPT models into orthopaedic education and underlines the necessity for specialized training of these models for specific medical domains.