Artificial Intelligence in Medical Biochemistry Laboratories: From Automation to Clinical Decision Support

Medical biochemistry laboratories are being rapidly transformed from rule-based automation environments into intelligent diagnostic ecosystems by artificial intelligence. Integration of Natural language processing, deep learning, machine learning, and expert systems and emerging agentic AI models across pre-analytical, analytical, and post-analytical laboratory workflows is highlighted in this review. Automated quality control monitoring, predictive maintenance of analysers, intelligent sample routing, auto verification of results, multimodal data interpretation, and enhanced clinical decision support are included among the unique contributions of AI in laboratory medicine. Further discussion is provided in the article on AI's contribution to increased diagnostic precision, turnaround time and operational efficiency. Obstacles pertaining to model interpretability, data quality, regulatory compliance, and workforce adaptation are addressed. Ethical considerations, data privacy safeguards, and governance requirements for AI deployment in laboratory settings are also outlined. Artificial Intelligence is not positioned as an alternative for laboratory experts, but instead presented as decision-support partner through which precision, safety, and clinical value in modern biochemistry services are enhanced.