ArticleCancer cytopathology2025
Assessment of the efficacy and accuracy of cervical cytology screening with the Hologic Genius Digital Diagnostics System.
Article in Cancer cytopathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for cervical cancer screening and diagnosis using Pap smear images: a systematic review.BMC cancer · 2026Pooled it
- Advanced molecular detection, bioinformatics, and artificial intelligence era in medical laboratory science education.Journal of microbiology & biology education · 2026Article
- How to evaluate and select cervical screening and triage tests in vaccinated populations.Journal of the National Cancer Institute. Monographs · 2026Article
- Enhancing efficiency and improving turnaround time: real-world impact of the Genius Digital Diagnostics System implementation.American journal of clinical pathology · 2026Article
- Technical considerations during validation of the Genius® Digital Diagnostic System.Journal of pathology informatics · 2026Article
- Translational AI in whole-slide image cancer histopathology: state of the art and regulatory-approved solutions.Frontiers in digital health · 2026Review
- Assessment of the efficiency and accuracy of an artificial intelligence assistive system in the diagnosis of Pap cervical atypical glandular cell cytology.Cancer cytopathology · 2025Article
- Assessment of the efficacy and accuracy of cervical cytology screening with the Hologic Genius Digital Diagnostics System.Cancer cytopathology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical technologies powered by artificial intelligence are quickly transforming into practical solutions by rapidly leveraging massive amounts of data processed via deep learning algorithms. There is a necessity to validate these innovative tools when integrated into clinical practice.
methodsThis study evaluated the performance of the Hologic Genius Digital Diagnostics System (HGDDS) with a cohort of 890 previously reviewed and diagnosed ThinPrep Papanicolaou (Pap) tests with the intent to deploy this system for routine clinical use. The study included all diagnostic categories of The Bethesda System, with follow-up tissue sampling performed within 6 months of abnormal Pap test results to serve as the ground truth.
resultsThe HGDDS demonstrated excellent performance in detecting significant Pap test findings, with close to 100% sensitivity (98.2%-100%) for cases classified as atypical squamous cells of undetermined significance and above within a 95% confidence interval and a high negative predictive value (92.4%-100%).
conclusionsThe HGDDS streamlined workflow, reduced manual workload, and functioned as a stand-alone system.
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Identifiers
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.