Abstract Artificial intelligence (AI) is ushering in a transformative era in laboratory diagnostics, addressing many limitations of traditional workflows characterized by manual processes, delays, and susceptibility to human error. This review provides valuable insights for clinicians, researchers, and policymakers, emphasizing the importance of responsible and evidence-based deployment of AI to enhance diagnostic accuracy, operational efficiency, and patient outcomes. This narrative review explores the evolution of laboratory diagnostics through the integration of AI technologies, highlighting the transition from conventional diagnostic approaches to data-driven and automated systems. The review introduces the core foundations of AI, including machine learning, deep learning, and natural language processing, and examines their role in advancing diagnostic accuracy and efficiency. Key AI learning paradigms—supervised, unsupervised, and reinforcement learning—are discussed in the context of pattern recognition, anomaly detection, and clinical decision support. The applications of AI across major laboratory domains, including hematology, pathology, clinical biochemistry, and microbiology, are comprehensively explored, demonstrating improvements in diagnostic precision, turnaround time, and workflow optimization. Furthermore, the integration of AI with laboratory information systems is highlighted as a critical enabler of streamlined operations and personalized patient care. Despite these advantages, challenges such as data bias, ethical concerns, model interpretability, and regulatory compliance remain significant barriers to widespread adoption. Looking ahead, the review outlines emerging directions, including multi-omics data integration, predictive analytics, and next-generation diagnostic frameworks, which have the potential to further redefine laboratory diagnostics. In addition to increasing automation, AI in laboratory diagnostics is progressively evolving toward an augmented intelligence paradigm where clinicians and AI systems collaboratively support safer and more reliable diagnostic decisions.
Agarwal et al. (Sun,) studied this question.