A conceptual framework integrating artificial intelligence with failure mode and effects analysis for diagnostic error reduction in anatomical pathology

Diagnostic errors in pathology remain a significant contributor to patient harm despite advances in laboratory automation and quality management systems. Failure Mode and Effects Analysis (FMEA) provides a structured approach to the prospective identification and prioritization of workflow risks, while artificial intelligence (AI) offers emerging capabilities in image analysis, data validation, and workflow monitoring. In this study, we surveyed 43 pathologists to characterize commonly encountered diagnostic errors across the pathology workflow and found that errors were most prevalent in the pre-examination phase. These findings informed an FMEA-based risk assessment that identified high-priority failure modes. Building on these insights, we propose a conceptual framework that integrates agentic AI with FMEA-driven quality management. In this model, AI systems can enhance error detection, reduce failures, and enable continuous recalibration of risk through dynamic data feedback. This work is intended as a hypothesis-generating and conceptual contribution. Prospective validation, standardized performance metrics, and real-world implementation studies will be necessary to determine the clinical and operational impact of such integrated systems.