Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps
TL;DR
AI has the capacity to strengthen diagnostic pathology by improving consistency, measurement and efficiency and moving from experimental use to routine reporting will require broad validation across centres, enhanced model transparency, strong quality‐assurance systems and close cooperation between developers and pathologists.
Abstract
Background Whole‐slide imaging (WSI) has shifted pathology toward digital workflows, creating the foundation for applying artificial intelligence (AI) to diagnostic tasks. This review summarises validated AI applications in diagnostic pathology, with an emphasis on clinical performance, regulatory developments and the practical barriers that affect implementation. Methods A structured search of PubMed, Scopus and Google Scholar identified English‐language, peer‐reviewed studies from January 2020 to May 2025. Eligible studies applied AI to diagnostic, grading or prognostic tasks in human tissue, used a pathologist‐confirmed reference standard and included external or multi‐centre validation. Results More than 150,000 digital slides were represented across the included studies. Reported performance metrics demonstrated strong diagnostic accuracy across several validated applications. Large meta‐analyses and externally validated studies reported sensitivity values exceeding 96% and specificity above 93% for selected cancer‐detection tasks, while other studies demonstrated high agreement for Gleason grading (QWK up to 0.862) and biomarker quantification (Ki‐67 ICC 0.98). Conclusion AI has the capacity to strengthen diagnostic pathology by improving consistency, measurement and efficiency. Moving from experimental use to routine reporting will require broad validation across centres, enhanced model transparency, strong quality‐assurance systems and close cooperation between developers and pathologists.