Artificial Intelligence in Medical Laboratory Medicine: Clinical Applications, Ethical Challenges, and the Evolving Role of Laboratory Professionals in Low-Resource Settings

Artificial intelligence (AI) is rapidly transforming medical laboratory medicine by enhancing diagnostic accuracy, workflow efficiency, and decision support across multiple laboratory disciplines. Medical laboratories generate large volumes of complex data, and traditional manual interpretation of test results remains vulnerable to human error, observer variability, and workload-related fatigue. These challenges are particularly pronounced in low-resource laboratory settings, where limited infrastructure, workforce shortages, and inconsistent quality control further compromise diagnostic reliability.
This review article critically examines current applications of artificial intelligence in routine medical laboratory testing, including hematology, clinical chemistry, and urinalysis. Existing literature demonstrates that AI-assisted systems can improve abnormal result detection, reduce inter-observer variation, enhance quality control monitoring, and support standardized result interpretation. However, the implementation of AI in laboratory medicine remains uneven, with significant barriers in low-resource environments such as limited digital infrastructure, financial constraints, data scarcity, and lack of AI-specific training.
Ethical challenges associated with AI adoption are also explored, including data privacy concerns, algorithmic bias, transparency, and accountability in diagnostic decision-making. AI models trained predominantly on datasets from high-income settings may underperform in underrepresented populations, potentially exacerbating healthcare disparities if not properly validated and contextualized.
Importantly, this review highlights the evolving role of Medical Laboratory Scientists (MLS) in the era of artificial intelligence. Rather than replacing laboratory professionals, AI redefines their responsibilities toward result validation, quality assurance, ethical oversight, and clinical interpretation. The article emphasizes the need for AI literacy, digital skills training, and regulatory frameworks to ensure responsible and effective AI integration.
Overall, this review provides a comprehensive and practical overview of the opportunities, limitations, and future directions of artificial intelligence in medical laboratory medicine, with a particular focus on low-resource settings.