aceEvaluation of six generative large language models answering pre-analytical questions in medical microbiology
Objectives: Large language models (LLMs) have the potential to be integrated into diagnostics workflows, yet their reliability in pre-analytical diagnostic stewardship remains uncertain. We aimed to evaluate the performance of six LLMs answering common pre-analytical questions in the domain of medical microbiology. Next, we propose a blueprint to evaluate LLMs in medical microbiology. Methods: We compared general-purpose models from OpenAI (GPT-3.5, GPT-4) and Meta AI (Llama2-7B, Llama2-13B), alongside two domai…
Six LLMs performed inconsistently on pre-analytical microbiology questions, and experienced specialists rated responses lower than trainees did, so the answers need critical appraisal before use.
The study shows that ratings varied markedly between professional groups, so perceived quality is not uniform and a single assessment of the models' reliability may be unreliable.
Evidence
- Peer-reviewedClinical Microbiology and Infection2026-10-13
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Truvace Impact Record TRV-2026-1354, v1: “Evaluation of six generative large language models answering pre-analytical questions in medical microbiology.” Truvace, 2026-10-14. /record/TRV-2026-1354 (accessed at citation time). sha256 72ec82b6e45e864e…
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