Role of Agentic Artificial Intelligence in Anatomical and Clinical Laboratory
Abstract
Background: Laboratory medicine is transitioning from passive machine learning models and static decision-support algorithms toward agentic artificial intelligence (AI)—systems capable of goal-directed autonomy, environmental perception, multi-step reasoning, dynamic tool utilization, and iterative self-correction. This review synthesizes the operational frameworks and clinical implications of agentic AI across anatomical pathology and clinical laboratories (Biochemistry, hematology, microbiology, and molecular diagnostics). In anatomical pathology, multi-agent frameworks optimize whole-slide imaging workflows through adaptive region-of-interest navigation, autonomous pre-screening, automated biomarker quantification, and integrated multi-modal report generation. In clinical laboratories, agentic architectures streamline pre-analytic specimen quality verification, orchestrate real-time delta-check evaluations, resolve atypical hematologic cell flagging, guide microbiology culture plate selection, and automate complex next-generation sequencing variant curation. By interconnecting Laboratory Information Systems, digital instrumentation, and electronic health records, multi-agent networks bridge data silos to enable cross-disciplinary diagnostic synthesis. Despite substantial gains in diagnostic throughput, turnaround times, and quality assurance, clinical implementation is constrained by computational overhead, potential error propagation in reasoning chains, data privacy, and the risk of automated "hallucinations". Addressing these challenges necessitates robust human-in-the-loop oversight, standardized audit trails for decision chains, and evolving regulatory compliance frameworks (FDA, CE-IVDR). Agentic AI represents a paradigm shift from narrow analytical tools to collaborative diagnostic teammates, positioned to elevate precision medicine and operational efficiency while redefining the roles of pathologists and laboratory scientists.