Background Large language models exhibiting human-level performance in specialized tasks are emerging; examples include Generative Pretrained Transformer 3.5, which underlies the processing of ChatGPT. Rigorous trials are required to understand the capabilities of emerging technology, so that innovation can be directed to benefit patients and practitioners. Objective Here, we evaluated the strengths and weaknesses of ChatGPT in primary care using the Membership of the Royal College of General Practitioners Applied Knowledge Test (AKT) as a medium. Methods AKT questions were sourced from a web-based question bank and 2 AKT practice papers. In total, 674 unique AKT questions were inputted to ChatGPT, with the model’s answers recorded and compared to correct answers provided by the Royal College of General Practitioners. Each question was inputted twice in separate ChatGPT sessions, with answers on repeated trials compared to gauge consistency. Subject difficulty was gauged by referring to examiners’ reports from 2018 to 2022. Novel explanations from ChatGPT—defined as information provided that was not inputted within the question or multiple answer choices—were recorded. Performance was analyzed with respect to subject, difficulty, question source, and novel model outputs to explore ChatGPT’s strengths and weaknesses. Results Average overall performance of ChatGPT was 60.17%, which is below the mean passing mark in the last 2 years (70.42%). Accuracy differed between sources (P=.04 and .06). ChatGPT’s performance varied with subject category (P=.02 and .02), but variation did not correlate with difficulty (Spearman ρ=–0.241 and –0.238; P=.19 and .20). The proclivity of ChatGPT to provide novel explanations did not affect accuracy (P>.99 and .23). Conclusions Large language models are approaching human expert–level performance, although further development is required to match the performance of qualified primary care physicians in the AKT. Validated high-performance models may serve as assistants or autonomous clinical tools to ameliorate the general practice workforce crisis.

BACKGROUND Large language models exhibiting human-level performance in specialised tasks are emerging; examples include GPT3.5 which underlies the processing of ChatGPT. OBJECTIVE Here, we evaluated the strengths and weaknesses of ChatGPT in primary care, using the MRGCP Applied Knowledge Test (AKT) as a medium. METHODS AKT questions were sourced from an online question bank and two AKT practice papers. 674 unique AKT questions were inputted to ChatGPT, with the model’s answers recorded and compared to correct answers provided by the RCGP. Each question was inputted twice, in separate ChatGPT sessions, with answers on repeated trials compared to gauge consistency. Subject difficulty was gauged by referring to examiners’ reports over the last five years. Novel explanations from ChatGPT—defined as information provided which was not inputted within the question of multiple answer choices—were recorded. Performance was analysed with respect to subject, difficulty, question source, and novel explanations to explore ChatGPT’s strengths and weaknesses. RESULTS Average overall performance was 60.17%, below the mean passing mark in the last two years (70.42%). Accuracy differed between sources (p=0.035, 0.059). ChatGPT’s performance varied with subject category (p=0.021, 0.015), but variation did not correlate with difficulty (ρ=-0.241, -0.238; p=0.191, 0.197). The proclivity of ChatGPT to provide novel explanations did not affect accuracy (p=1.000, 0.233). CONCLUSIONS Large language models are approaching human expert-level performance, although further development is required to match qualified primary care physicians in the AKT. Validated high-performance models may serve as assistants or autonomous clinical tools to ameliorate the general practice workforce crisis.