Review traces how AI in lab medicine moved from basic models to AI chatbots

Study title: Artificial intelligence in laboratory medicine: From machine learning to large language models

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.

Read the study abstract

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…

By the Truvace deskSeptember 15, 20261 min read1 sourceEvidence: Emerging · 30/100 — Commentary, opinion, or narrative review · 1 independent sourcePeer-reviewedTRV-2026-1093· cite & verify

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In brief

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.

Main 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.

The gain

AI systems including large language models can process unstructured clinical text and interpret complex laboratory findings to support clinical decision-making in laboratory medicine.

The problem

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.

The 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.

What this doesn’t fix

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.