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TRUVACE RECORD VERSION
record: TRV-2026-1093
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-15T06:55:11.150600Z
status: published
lens: trace
sector: health
headline: Artificial intelligence in laboratory medicine: From machine learning to large language models
dek: 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…
gain_title: AI systems including large language models can process unstructured clinical text and interpret complex laboratory findings to support clinical decision-making in laboratory medicine.
problem_title: 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.
trace_subject: AI deployment for laboratory medicine interpretation and clinical decision support
gain_reading: AI systems including large language models can process unstructured clinical text and interpret complex laboratory findings to support clinical decision-making in laboratory medicine.
gain_evidence: capable of processing unstructured clinical text, interpreting complex laboratory findings, and supporting clinical decision-making
problem_reading: 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.
problem_evidence: Laboratories contemplating AI adoption face a distinctive set of challenges: preanalytical variability, interplatform calibration differences, effects of the specimen quality, reagent-lot sensitivity, and the context dependence of result interpretation
quick_read: This narrative review from Chinese Medical Journal examines how AI in laboratory medicine has evolved from conventional machine learning on structured results to deep learning and large language models that handle unstructured clinical text. It compares four paradigms and reviews evidence across blood cell morphology, autoverification, infectious risk stratification, urinalysis interpretation, decision support, and report generation.

The shift matters because laboratory results drive clinical decisions but are sensitive to preanalytical and analytical factors that general medical AI does not encounter. The authors argue this creates a higher bar for validation and oversight, leaving open how to build and evaluate laboratory-specific foundation models that integrate results, longitudinal data, QC metadata, and instrument data while keeping accountability with laboratory professionals.
limitation: 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.
tag: Dual reading
key_points: Review compares four AI paradigms: conventional machine learning, deep learning, general-purpose large language models, and laboratory-specific foundation models. | Application domains examined include blood cell morphological analysis, autoverification, acute infectious risk stratification, urinalysis interpretation, clinical decision support, and report generation. | Authors argue near-term deployment should be assistive with human-in-the-loop oversight, clear problem definition, external validation, prospective silent trials, and post-deployment surveillance aligned with laboratory quality systems.
rundown: The review traces a decade-long shift from traditional machine learning on structured lab data to large language models handling unstructured text, and proposes vertically oriented laboratory-specific foundation models integrating structured results, longitudinal patient data, quality-control metadata, instrument information, and clinical knowledge.

It frames deployment as an assistive layer rather than autonomous agent, emphasizing that clinical accountability must remain with qualified laboratory professionals and that implementation must align with existing laboratory quality systems.
sources:
- peer_reviewed | Chinese Medical Journal | https://doi.org/10.1097/cm9.0000000000004291 | 2026-09-14
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