ReviewDiscover oncology2025
Progress in the application research of cervical cancer screening developed by artificial intelligence in large populations.
Review in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Predicting lifetime cervical cancer screening among sexually active women in low- and middle-income countries using machine learning models: evidence from Demographic and Health Surveys.AJOG global reports · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- SegResDeiT: a hybrid SegNet-ResNet-50-DeiT framework for automated cervical cancer segmentation and classification.BMC medical imaging · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Cervical cancer stands out among various cancers due to its potential for prevention and eradication, mainly through vaccination and proactive screening measures. However, there are still a large number of women in low- and middle-income countries who need to undergo cervical cancer screening. Conventional cervical cancer screening approaches possess distinct benefits and drawbacks regarding sensitivity, specificity, promptness, and expense. In recent years, artificial intelligence (AI) has gained widespread use to help healthcare professionals in performing extensive cervical cancer screenings, focusing on machine learning (ML) and deep learning (DL) techniques. Traditional screening methods combined with AI technology have shown initial effectiveness in cervical cancer screening. But it is necessary to address various challenges such as limited technology and resources, difficulties in integrating clinical workflows, and ethical and legal risks in large-scale population cervical cancer screening. In this review, how AI helps simplify workflows, aids in cytological segmentation and diagnosis, enhances the triage and diagnosis processes for human papillomavirus (HPV) and colposcopy were described firstly. Then we summarized the existing clinical cases of AI applied to large-scale cervical cancer screening. Finally, we discussed the challenges and limitations of AI implementation in large population cervical cancer screening. These insights may possess the capacity to transform cervical cancer screening on a global scale by improving diagnostic precision, facilitating early intervention, and increasing the overall efficacy of cervical cancer screening initiatives worldwide.
Indexed as
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.