Role of Machine Learning in Diagnostic Pathology and Laboratory Medicine: A Narrative Review

Abstract

Objective: Disease screening and diagnosis provide common issues for pathology and clinical laboratory medicine. These challenges include growing workloads, staffing shortages, diagnostic variability, and error susceptibility across the entire testing process. Although machine learning (ML) has been extensively used in image analysis and laboratory automation, studies that have already been written frequently focus on these areas independently, which prevents a comprehensive understanding of how ML affects diagnostic practice. This narrative review's goal is to compile the most recent data on machine learning applications in pathology and clinical laboratory medicine, with a focus on workflow efficiency, diagnostic performance, and the identification of laboratory errors throughout the entire testing process. Methods: Using predetermined search terms associated with machine learning, pathology, clinical laboratory medicine, and diagnostic quality, we performed a narrative review of studies indexed in PubMed, Scopus, Web of Science, and IEEE Xplore. Four domains diagnostic image analysis, biomarker and outcome prediction, workflow automation, and laboratory error detection are combined to create ML applications. Applying deep learning (DL) models to whole-slide images (WSIs) in pathology has produced results that are on par with pathologists in controlled environments. By utilizing multianalyte and temporal patterns, ML-based systems in clinical laboratory medicine improve automated verification, patient-based quality control (QC), and error identification, frequently exceeding conventional rule-based techniques. Prognostic modeling and decision support are further enhanced by new multimodal frameworks that incorporate laboratories, clinical, and histopathologic data. Conclusions: This study emphasizes the potential and constraints of machine learning (ML) in diagnostic medicine, stressing the necessity of interpretability, generalizability, and regulatory supervision to enable clinically significant, integrated implementation.