LLM-as-a-judge for infection prevention and control and antimicrobial resistance impact: comparing three main LLMs vs. human experts' assessment.

Background: Large language models (LLMs) are increasingly used to generate health information, yet their reliability as evaluators remains unclear. This study investigated the feasibility of an LLM-as-a-judge methodology in the context of infection prevention and antimicrobial resistance (AMR), comparing automated ratings with human expert benchmarks. Methods: We performed a secondary analysis of an expert-annotated dataset of health messages. Three leading LLMs (ChatGPT, Claude, Gemini) independently evaluated the same messages using an adapted DISCERN tool across five domains: information reliability, quality, AMR impact, persuasiveness, and overall score. We utilized descriptive statistics, intra-rater reliability tests, and mixed-effects ordinal regression to analyze divergence between automated and human assessments, adhering to CHART reporting guidelines. Results: Analysis of 404 evaluations revealed a systematic upward divergence: all LLMs consistently assigned higher scores than human experts. This optimism bias persisted after adjusting for domain-specific differences and clustering effects. The gap was particularly pronounced in domains of persuasiveness and AMR impact, while information quality showed more heterogeneous results. Intra-rater reliability assessments demonstrated that LLMs maintained stable scoring patterns under identical prompting conditions. Conclusions: LLMs exhibit a consistent leniency bias, systematically overestimating the quality of AMR-related health communication compared to human evaluators. These results do not support the use of LLMs for autonomous evaluation in high-stakes public health contexts. Rather, LLM-based judging is best suited as a scalable screening tool within supervised human-in-the-loop workflows, where expert oversight serves as a necessary safeguard for evidence-based accuracy.