Machine Learning Integration in Pathology Laboratories: Enhancing Diagnostic Accuracy and Workflow Efficiency in Low-Resource Healthcare Settings

Abstract

The integration of machine learning (ML) in pathology laboratories offers significant potential to improve diagnostic accuracy and optimize workflow efficiency, particularly in low-resource healthcare settings. This study investigates the effects of ML adoption on laboratory performance and professional competence among pathology staff in Pakistan. A cross-sectional survey was conducted among 400 pathology professionals, incorporating structured questionnaires measuring ML usage, diagnostic accuracy, workflow efficiency, and digital competence. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess direct and mediated relationships. Results indicate that ML adoption significantly enhances diagnostic accuracy (β = 0.54, p < 0.001) and workflow efficiency (β = 0.48, p < 0.001), with digital competence moderating the relationship between ML usage and professional autonomy. Ethical awareness also mediated the impact of ML adoption on diagnostic decision-making. These findings suggest that ML can substantially improve laboratory outcomes in low-resource settings, provided that staff training and ethical protocols are implemented. Policymakers and healthcare administrators should prioritize infrastructure investment, competency development, and context-specific ML integration strategies to maximize benefits. This study provides empirical evidence for scaling AI-enabled laboratory services in Pakistan and similar healthcare environments.