aceArtificial intelligence in laboratory medicine: From machine learning to large language models
Abstract The integration of artificial intelligence (AI) into laboratory medicine has undergone a remarkable evolution over the past decade, shifting from traditional machine learning (ML) applied to structured laboratory data to the recent emergence of large language models (LLMs) capable of processing unstructured clinical text, interpreting complex laboratory findings, and supporting clinical decision-making. This narrative review traces that progression and critically compares four main AI paradigms accessib…
AI systems including large language models can process unstructured clinical text and interpret complex laboratory findings to support clinical decision-making in laboratory medicine.
Laboratories adopting AI face distinctive challenges including preanalytical variability, interplatform calibration differences, specimen quality effects, reagent-lot sensitivity, and context-dependent result interpretation that complicate validation and deployment.
Laboratory AI requires validation beyond general medical AI due to preanalytical variability, interplatform calibration differences, specimen quality, reagent-lot sensitivity, and context-dependent interpretation, plus need for external validation and silent trials.
Evidence
- Peer-reviewedChinese Medical Journal2026-09-14
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Truvace Impact Record TRV-2026-1093, v1: “Artificial intelligence in laboratory medicine: From machine learning to large language models.” Truvace, 2026-09-15. /record/TRV-2026-1093 (accessed at citation time). sha256 1e5ad1569f942872…
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