GP or ChatGPT? Ability of large language models (LLMs) to support general practitioners when prescribing antibiotics.

Abstract

Introduction: Large language models (LLMs) are becoming ubiquitous and widely implemented. LLMs could also be used for diagnosis and treatment. National antibiotic prescribing guidelines are customized and informed by local laboratory data on antimicrobial resistance.

Methods: Based on 24 vignettes with information on type of infection, gender, age group and comorbidities, GPs and LLMs were prompted to provide a treatment. Four countries (Ireland, UK, USA and Norway) were included and a GP from each country and six LLMs (ChatGPT, Gemini, Copilot, Mistral AI, Claude and Llama 3.1) were provided with the vignettes, including their location (country). Responses were compared with the country's national prescribing guidelines. In addition, limitations of LLMs such as hallucination, toxicity and data leakage were assessed.

Results: GPs' answers to the vignettes showed high accuracy in relation to diagnosis (96%-100%) and yes/no antibiotic prescribing (83%-92%). GPs referenced (100%) and prescribed (58%-92%) according to national guidelines, but dose/duration of treatment was less accurate (50%-75%). Overall, the GPs' accuracy had a mean of 74%. LLMs scored high in relation to diagnosis (92%-100%), antibiotic prescribing (88%-100%) and the choice of antibiotic (59%-100%) but correct referencing often failed (38%-96%), in particular for the Norwegian guidelines (0%-13%). Data leakage was shown to be an issue as personal information was repeated in the models' responses to the vignettes.

Conclusions: LLMs may be safe to guide antibiotic prescribing in general practice. However, to interpret vignettes, apply national guidelines and prescribe the right dose and duration, GPs remain best placed.