Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin· 2026Q1
Validation of diagnostic artificial intelligence models for prostate pathology in a Middle Eastern cohort
- 1citations
- Q1SCImago
- 2026year
Short summary
AI models for prostate cancer diagnosis and Gleason grading achieved concordance with human pathologists (kappa 0.801) comparable to pathologist-pathologist agreement (kappa 0.799) in a Middle Eastern cohort.
AI-generated from the title and abstract; the full text is not read.
Key points
- AI models achieved pathologist-level concordance (kappa 0.801) for prostate cancer diagnosis and Gleason grading in a Middle Eastern cohort.
- AI-pathologist concordance was comparable to pathologist-pathologist concordance (kappa 0.799).
- AI models demonstrated high cross-scanner concordance (kappa > 0.90) across three different scanners.
- A dataset of 1,017 whole-slide images from 339 slides was generated and made publicly available.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract 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.
The authors' abstract, as published at the source. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Artificial Intelligence
Artificial IntelligenceComputer Science