What type of study is this?

This is a Systematic Review study.

What question did this study set out to answer?

To systematically review current AI research and tools in nongynecologic cytology and assess their effectiveness.

March 22, 2026

Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools

Key Points

To systematically review current AI research and tools in nongynecologic cytology and assess their effectiveness.
Reviewed literature from MEDLINE, Embase, and Cochrane Library from 2010 to 2024.
Screened approximately 24,000 records, identifying 71 studies meeting inclusion criteria.
Evaluated commercial AI platforms based on regulatory documents and vendor validation data.
Many studies showed diagnostic performance exceeding 90% accuracy.
Patch-level accuracy often reached around 94%, surpassing whole slide image metrics.
Reduced interobserver variability observed, with accuracies of 95.9% for senior pathologists and 94.4% for juniors.

Abstract

Abstract Background Nongynecologic (non‐GYN) cytology plays an important role in cellular and molecular cancer diagnosis, although its use is limited by variability and interobserver discrepancies. Recent studies suggest that artificial intelligence (AI) may improve diagnostic consistency and performance. Background This systematic review evaluated contemporary AI research in non‐GYN cytology and examined commercially available systems. Methods MEDLINE, Embase, and the Cochrane Library were searched for English‐language studies published from January 2010 to September 2024. Of ∼24,000 records screened, 71 met inclusion criteria. Commercial platforms (KFBIO, Landing Med, VitaDx/VisioCyt, AIxMED, and CellsVision) were assessed using publicly available regulatory documents, technical specifications, and vendor‐reported validation data. Results Many studies reported high internal diagnostic performance, often exceeding 90% accuracy. Patch‐level performance often reached ∼94%, exceeding whole slide image‐level metrics. Reduced interobserver variability was frequently observed, with accuracies of 95.9% for senior and 94.4% for junior pathologists. Several studies documented shorter diagnostic time. Thyroid (37%) and urinary bladder (30%) cytology were most frequently studied. AI applications included categorization, segmentation, and atypia detection, with recent work exploring immunocytochemistry support, mutation‐associated pattern recognition, and indirect prediction of histologic features. Conclusion AI in non‐GYN cytology shows promise but remains limited by data set quality, weak external validation, and regulatory barriers. At present, AI tools function primarily as decision‐support systems. Advancing clinical adoption will require multicenter validation, standardized data sets, and careful integration with expert interpretation.

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