Comparing the Performance of ChatGPT and GPT-4 versus a Cohort of Medical Students on an Official University of Toronto Undergraduate Medical Education Progress Test

Background Large language model (LLM) based chatbots have recently received broad social uptake; demonstrating remarkable abilities in natural language understanding, natural language generation, dialogue, and logic/reasoning. Objective To compare the performance of two LLM-based chatbots, versus a cohort of medical students, on a University of Toronto undergraduate medical progress test. Methods We report the mean number of correct responses, stratified by year of training/education, for each cohort of undergraduate medical students. We report counts/percentages of correctly answered test questions for each of ChatGPT and GPT-4. We compare the performance of ChatGPT versus GPT-4 using McNemar’s test for dependent proportions. We compare whether the percentage of correctly answered test questions for ChatGPT or GPT-4 fall within/outside the confidence intervals for the mean number of correct responses for each of the cohorts of undergraduate medical education students. Results A total of N=1057 University of Toronto undergraduate medical students completed the progress test during the Fall-2022 and Winter-2023 semesters. Student performance improved with increased training/education levels: UME-Year1 mean=36.3%; UME-Year2 mean=44.1%; UME-Year3 mean=52.2%; UME-Year4 mean=58.5%. ChatGPT answered 68/100 (68.0%) questions correctly; whereas, GPT-4 answered 79/100 (79.0%) questions correctly. GPT-4 performance was statistically significantly greater than ChatGPT (P=0.034). GPT-4 performed at a level equivalent to the top performing undergraduate medical student (79/100 questions correctly answered). Conclusions This study adds to a growing body of literature demonstrating the remarkable performance of LLM-based chatbots on medical tests. GPT-4 performed at a level comparable to the best performing undergraduate medical student who attempted the progress test in 2022/2023. Future work will investigate the potential application of LLM-chatbots as tools for assisting learners/educators in medical education. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The University of Toronto Research Ethics Board provided approval for conducting the study (REB-ID: 00044429). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes University of Toronto Undergraduate Medical Education Progress Test questions (input prompts to ChatGPT/GPT-4) are considered proprietary information, and are not available publicly.