SynthesisCancer cytopathology2026
Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools.
Synthesis in Cancer cytopathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundNongynecologic (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.
backgroundThis systematic review evaluated contemporary AI research in non-GYN cytology and examined commercially available systems.
methodsMEDLINE, 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.
resultsMany 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.
conclusionAI 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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.