ArticleInternational journal of gynecological cancer : official journal of the International Gynecological Cancer Society2023
Artificial intelligence and visual inspection in cervical cancer screening.
Article in International journal of gynecological cancer : official journal of the International Gynecological Cancer Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06452004 (Prevention and Screening Intervention Project - Towards Elimination of Cervical Cancer), which is not on this map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Prevention and Screening Intervention Project - Towards Elimination of Cervical Cancer: Validation of Artificial Intelligence as Decision Support System in VIA
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- A bibliometric analysis and visualization of trends in cervical cancer screening technologies based on dual-database analysis.Frontiers in oncology · 2026Pooled it
- Applying an AI Decision Support Device During Cervical Cancer Screening in Bangladesh and Uganda: Implementation Study.JMIR formative research · 2026Trial
- Frugal AI is the missing piece in cervical cancer screening.Nature medicine · 2026Article
- 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
- Evaluation of the Diagnostic Accuracy of Cervical Cell Morphologies from Android Device-Captured Cytopathological Microscopic Images through Artificial Intelligence in Mainly Rural or Resource-Constraint Areas of India.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Article
- Artificial intelligence and digital health equity: a post-pandemic evidence synthesis and implementation safeguards framework.Frontiers in digital health · 2026Review
- Artificial intelligence-driven screening, early diagnosis, and treatment strategies for cervical cancer: an overview.Infectious agents and cancer · 2025Review
- Evaluating the Cost-Effectiveness of Cervical Cancer Screening and Treatment in Western Romania.Current oncology (Toronto, Ont.) · 2025Article
- Review
- Diagnostic Accuracy of Artificial Intelligence vs. Oncologist Interpretation in Digital Cervicography for Abnormal Cervical Cytology.Journal of clinical medicine · 2025Article
- Article
- A novel and cost-effective model to screen and treat cervical cancer and precancers at the point of care.Frontiers in public health · 2025Article
- Development and Clinical Validation of Visual Inspection With Acetic Acid Application-Artificial Intelligence Tool Using Cervical Images in Screen-and-Treat Visual Screening for Cervical Cancer in South India: A Pilot Study.JCO global oncology · 2024Article
- Advancements in the Management of Cervical Intraepithelial Neoplasia: A Comprehensive Review.Cureus · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionVisual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.
methodsA total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.
resultsSensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.
conclusionThis study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.
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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.