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Life sciences · Journal article
Bulletin of the National Research Centre/bulletin of the National Research Center · September 1, 2026
A consensus or society position rather than new primary data.
This scoping review systematically maps 47 peer-reviewed studies on large language model applications in infectious disease diagnosis and antimicrobial prescribing. The evidence suggests LLMs can support medical knowledge and case reasoning at or near passing thresholds and achieve diagnostic accuracy comparable to physicians in vignette studies, but prescribing performance is inconsistent with modest agreement with specialists and degradation with case complexity. The authors recommend an assistive, human-supervised role with standardized evaluation, local grounding, and stewardship oversight before clinical adoption.
Scoping review (PRISMA-ScR). Peer-reviewed published studies of large language models used for clinical decision support in infectious disease diagnosis or antimicrobial prescribing.. Intervention: Large language models applied to infectious disease diagnosis, antimicrobial prescribing and stewardship, resistance and mechanism prediction, consultation, and ethics evaluation.. Compared with: Physician performance (vignette studies) and infectious disease specialist recommendations (prescribing studies).. n = 47.
Forty-seven sources included and mapped to five application domains: diagnosis, prescribing, resistance prediction, consultation, and ethics. LLMs achieved diagnostic accuracy comparable to physicians in vignette studies. Prescribing performance was inconsistent, with modest agreement with infectious disease specialists.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Clinicians and stewardship programmes should view LLMs as assistive tools requiring human oversight rather than autonomous prescribing agents. Implementation requires standardized local evaluation, grounding in local guidelines, and explicit stewardship governance before clinical adoption.
A systematic scoping review mapping the breadth, applications, and limitations of LLMs in infectious-disease diagnosis and prescribing, concluding with recommendations for safe adoption in clinical practice.
As stated by the source record.
Clinicians and stewardship programmes should view LLMs as assistive tools requiring human oversight rather than autonomous prescribing agents. Implementation requires standardized local evaluation, grounding in local guidelines, and explicit stewardship governance before clinical adoption.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
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.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.