Opportunities, pitfalls, ethics, and clinical governance of AI in laboratory medicine

Artificial intelligence (AI) is rapidly transforming healthcare, including laboratory medicine, by enabling new approaches to data analysis, automation, clinical decision support, and integration of complex biological information. AI applications now extend across the entire testing process, including test requisition, pre-analytical workflow optimization, laboratory automation, analytical quality management, post-analytical validation, and interpretation. Although AI provides considerable potential to improve efficiency, accuracy, standardization, and personalized medicine, its routine implementation in clinical laboratories remains challenging. Unlike traditional laboratory technologies, AI systems depend on the quality, representativeness, and reliability of the data used for model development, validation, and continuous improvement. High technical performance alone is not a guarantee of clinical effectiveness and patient safety. Successful integration of AI requires appropriate algorithm development and validation, continuous performance monitoring, evaluation of bias and robustness, and adherence to strict regulatory and governance frameworks. Additional key challenges include data quality, interoperability, explainability, cybersecurity, and maintenance of skill and professional responsibility throughout the entire clinical decision-making process. Therefore, this perspective article explores the current and future landscape of AI across the entire testing process, highlighting the importance of distinguishing technological efficiency from clinical benefits. Ethical considerations, regulatory frameworks, and practical strategies for safe implementation will also be discussed. At this stage of development, we conclude that AI should be considered a supportive technology designed to complement and strengthen the expertise of laboratory professionals, rather than a replacement for human clinical judgment and professional decision-making, while its future success will depend on accurate development and validation, transparent governance and continued commitment to evidence-based, patient-centered laboratory practice.