Validation of diagnostic artificial intelligence models for prostate pathology in a Middle Eastern cohort.
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Muhammad Ali PJ, Vincent N, Abdulla SS, Mohammed Fadhl HN, Blilie A, Szolnoky K, Mielcarz JA, Ji X, Mulliqi N, Al-Talabani AK, Kartasalo K
Virchows Arch. - (-) - [2026-10-10; online 2026-10-10]
Artificial intelligence (AI) for pathology is accelerating cancer diagnostics. For global adoption, studies in currently under-represented populations - where the potential gains from AI may also be greatest - are needed. We present the first external validation study in a Middle Eastern setting, focusing on diagnosis and Gleason grading of prostate cancer. We digitised 339 prostate glass slides from Kurdistan, Iraq, representing a consecutive series of 185 patients from 2013 to 2024. Each slide was scanned on three digital pathology scanners, generating 1,017 whole-slide images. Task-specific and foundation AI models demonstrated AI-pathologist grading concordance similar to pathologist-pathologist concordance (Cohen's quadratically weighted kappa 0.801 vs. 0.799). Cross-scanner concordance was high (kappa > 0.90) for all AI models across three scanners. The demonstrated robust performance on a low-cost compact scanner could enable validation studies in non-digitalised settings and cost-effective AI adoption in laboratories with limited sample volumes. The publicly shared dataset supports further research to facilitate broader adoption of AI pathology.
PubMed 42858194
DOI 10.1007/s00428-026-04742-5
Crossref 10.1007/s00428-026-04742-5
pii: 10.1007/s00428-026-04742-5