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Peer-reviewedCC BY-NC-ND 4.0Diagnostic Pathology

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

  • Poombal1
  • Raees Abbas Lail2
  • Swati Satturwar3
  • Rajendra Singh4
  • Ehsan Ullah3,5
  • Anil V. Parwani3
5 affiliations
  1. 1Department of Pathology, UMass Chan Medical School-Baystate Medical Center, Springfield, MA USA
  2. 2Department of Pathology, Sahiwal Medical College, Sahiwal, Punjab Pakistan
  3. 3Department of Pathology, College of Medicine, Ohio State University, Columbus, OH USA
  4. 4Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA USA
  5. 5Surgical Division, Middlemore Hospital, Health New Zealand, Counties District, Auckland, New Zealand
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{poombal2026conceptual,
  title = {{A conceptual framework integrating artificial intelligence with failure mode and effects analysis for diagnostic error reduction in anatomical pathology}},
  author = {Poombal and Raees Abbas Lail and Swati Satturwar and Rajendra Singh and Ehsan Ullah and Anil V. Parwani},
  journal = {Diagnostic Pathology},
  year = {2026},
  month = aug,
  doi = {10.1186/s13000-026-01816-5},
  url = {https://doi.org/10.1186/s13000-026-01816-5},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - A conceptual framework integrating artificial intelligence with failure mode and effects analysis for diagnostic error reduction in anatomical pathology
AU  - Poombal
AU  - Raees Abbas Lail
AU  - Swati Satturwar
AU  - Rajendra Singh
AU  - Ehsan Ullah
AU  - Anil V. Parwani
PY  - 2026
DA  - 2026/08/07
JO  - Diagnostic Pathology
DO  - 10.1186/s13000-026-01816-5
UR  - https://doi.org/10.1186/s13000-026-01816-5
AB  - 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.
SN  - 1746-1596
ER  -