rastinza @rastinza@qoto.org

We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any external 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 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data analyzed in this study were obtained from USMLE sample questions sets which are publicly available. The question index, raw inputs, and raw AI outputs are available in the Online Data Supplement. Inquiries and requests for additional dataset items and adjudication results can be provided upon reasonable request by contacting Victor Tseng, MD (victor{at}ansiblehealth.com). * ACI : Accuracy-Concordance-Insight scoring system DOI : Density of insight GPT : Generative pretrained transformer LLM : Large language model MCSA : Multiple choice single answer MC-J : Multiple choice single answer with forced justification MC-NJ : Multiple choice single answer without forced justification NLP : Natural language processing OE : Open-ended question formulation Q n . m : Question n , input run m USMLE : United States Medical Licensing Exam

Gas chromatography-mass spectrometry ID'd the culprit as the volatile ketone 3-octanone.