The Impact Of Advances In Laboratory Technology On The Practice Of Laboratory Medical Technologists: A Systematic Review Of Diagnostic Performance And Workflow Integration

Abstract

Advances in medical laboratory technology have rapidly reshaped the professional roles, competencies, and diagnostic contributions of Laboratory Medical Technologists (LMTs). This systematic review evaluates how modern technological developments impact (1) diagnostic performance outcomes, including accuracy and error reduction, and (2) laboratory workflow integration, automation adoption, and coordination with clinical units. Following the PRISMA 2020 Statement framework, peer-reviewed publications from 2016–2025 were systematically searched and synthesized from major scientific databases. Eligible studies emphasized technologies including laboratory automation systems, artificial intelligence (AI)-assisted diagnostics, digital pathology platforms, and smart laboratory information systems (LIS) integrated with electronic health records (EHRs). Findings indicate consistent improvements in diagnostic accuracy, turnaround time efficiency, quality control reliability, and reduction of pre-analytical and analytical errors when LMT practices are supported by intelligent and automated technologies. Technological evolution also expanded professional competencies in data interpretation, LIS governance, and cross-department coordination, reinforcing LMTs’ central role in clinical decision support. However, challenges remain in training gaps for emerging tools, system interoperability limitations, and standardization of new digital competencies across institutions. The review concludes that laboratory technology advancement enhances LMT-driven diagnostic performance and workflow integration but requires structured competency frameworks, continuous training, and governance policies to sustain quality and safety gains. Recommendations focus on adopting standardized digital skill blocks, AI-enabled quality governance, and LIS integration strategies aligned with institutional maturity models.