Performance Evaluation of the Generative Pre-trained Transformer (GPT-4) on the Family Medicine In-Training Examination.

Abstract

Objective: In this study, we sought to comprehensively evaluate GPT-4 (Generative Pre-trained Transformer)'s performance on the 2022 American Board of Family Medicine's (ABFM) In-Training Examination (ITE), compared with its predecessor, GPT-3.5, and the national family residents' performance on the same examination.

Methods: We utilized both quantitative and qualitative analyses. First, a quantitative analysis was employed to evaluate the model's performance metrics using zero-shot prompt (where only examination questions were provided without any additional information). After this, qualitative analysis was executed to understand the nature of the model's responses, the depth of its medical knowledge, and its ability to comprehend contextual or new information through chain-of-thoughts prompts (interactive conversation) with the model.

Results: This study demonstrated that GPT-4 made significant improvement in accuracy compared with GPT-3.5 over a 4-month interval between their respective release dates. The correct percentage with zero-shot prompt increased from 56% to 84%, which translates to a scaled score growth from 280 to 690, a 410-point increase. Most notably, further chain-of-thought investigation revealed GPT-4's ability to integrate new information and make self-correction when needed.

Conclusions: In this study, GPT-4 has demonstrated notably high accuracy, as well as rapid reading and learning capabilities. These results are consistent with previous research indicating GPT-4's significant potential to assist in clinical decision making. Furthermore, the study highlights the essential role of physicians' critical thinking and lifelong learning skills, particularly evident through the analysis of GPT-4's incorrect responses. This emphasizes the indispensable human element in effectively implementing and using AI technologies in medical settings.