Assessing the Performance of Artificial Intelligence on Anesthesiology In-Training Examinations and Applicability in Medical Education

Introduction

Large language models (LLMs) have demonstrated substantial performance on medical licensing and board examinations, but their application to anesthesiology remains less well studied. Prior evaluations have not compared current-generation models from different developers or assessed performance on figure-dependent questions that earlier models lacked the capability to interpret. This study evaluates the performance of two current-generation LLMs on a comprehensive anesthesiology in-training examination (ITE) review question bank, including figure-dependent items.