Large language models for clinical decision support in infectious disease diagnosis and antimicrobial prescribing: a scoping review

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Abstract Background Antimicrobial resistance is one of the leading threats to global health. The inappropriate use of antibiotics is one of its strongest drivers. Large language models (LLMs) have entered clinical discussion as tools that might support diagnosis and prescribing. However, their specific role in infectious-disease (ID) care has not been mapped in a structured way. This scoping review charts the breadth, applications, performance signals, and limitations of LLMs used for clinical decision support in ID diagnosis and antimicrobial prescribing. Methods We followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR). We searched PubMed/MEDLINE for peer-reviewed sources that described LLM-based decision support in ID diagnosis or antimicrobial use. Sources were charted by application domain, model evaluated, study design, reported outcomes, and stated limitations. Findings were summarized descriptively. Inferential statistics are reported only as stated by the primary studies. Results Forty-seven sources were included. They were mapped to five domains: diagnosis and clinical reasoning; antimicrobial prescribing and stewardship; resistance and mechanism prediction; consultation and disease-specific management; and mitigation, evaluation, and ethics. LLMs answered medical-knowledge and case questions at or near passing thresholds. In vignette studies, they achieved diagnostic accuracy comparable to physicians. However, prescribing performance was inconsistent. Agreement with ID specialists on antibiotic choice was often modest, accuracy fell as case complexity rose, and unsafe or guideline-discordant advice recurred. Retrieval-augmented generation and domain grounding consistently improved accuracy and reduced hallucination. Conclusions Current evidence supports an assistive, human-supervised role for LLMs in ID care rather than autonomous prescribing. Standardized evaluation, prospective validation, local grounding, and explicit stewardship oversight are prerequisites for safe adoption. Clinical trial registration Not applicable. This study is a scoping review of existing literature and is not a clinical trial; therefore, no clinical trial registration number is applicable.