Evaluating the Diagnostic Performance and Clinical Interpretability of Explainable AI (XAI) in Digital Histopathology: A Meta-Analysis of Diagnostic Performance

Authors

• Bandar S. Alshreef, Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Shaqra, Kingdom of Saudi Arabia
• Yousif A. Kariri, Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Shaqra, Kingdom of Saudi ArabiaFollow

Abstract

Objective: To explore peer-reviewed research on the application of Explainable Artificial Intelligence (XAI) techniques for histopathology-driven cancer risk evaluation, and to address the limitations delaying its transition into routine clinical pathology practice (such as model reliability, clinical interpretability, and governance oversight).
Methods: We conducted a systematic review with quantitative meta-analysis of studies evaluating explainable artificial intelligence (XAI) in histopathology. Searches were performed in Web of Science, Scopus, PubMed, and Google Scholar through November 2025. Eligible studies were assessed for diagnostic performance, explainability approaches, and methodological quality. Of the 47 included studies, 9 provided sufficient quantitative data for meta-analysis. Post-hoc visual attribution approaches were the most frequently reported explanation methods in the included literature, with Class Activation Map (CAM) and Gradient-weighted (Grad) -CAM being the most prevalent.
Results: Nine studies provided sufficient quantitative data for meta-analysis. The combined area beneath the curve was 0.962 (95% confidence intervals (CI): 0.909–0.985), despite significant inconsistency among the selected studies (evolution of heterogenicity (I²) = 97.1%). The investigation of subgroup revealed that models tested at the slide level performed better and more consistently than those examined at the patient level, suggesting that the unit of analysis has a significant effect on reported accuracy and heterogeneity.
Conclusion: Future clinical translation of XAI in histopathology must prioritize explanation methods that are meaningful to pathologists, conduct usability testing in real diagnostic workflows, ensure prospective validation, and establish implementation within quality-management and governance frameworks.

Article Type

Systematic Review

First Page

1697

Last Page

1710

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Recommended Citation

Alshreef, Bandar S. and Kariri, Yousif A. (2026) "Evaluating the Diagnostic Performance and Clinical Interpretability of Explainable AI (XAI) in Digital Histopathology: A Meta-Analysis of Diagnostic Performance," Saudi Medical Journal: Vol. 47: Iss. 11, Article 2.
DOI: https://doi.org/10.15537/1658-3175.8859