ArticleJournal of the National Cancer Institute2024
Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening.
Article in Journal of the National Cancer Institute, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Automated acquisition of explainable knowledge from unannotated colposcopic images for predicting the natural course of CIN2.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Diagnosis and treatment of female genital schistosomiasis.The Lancet. Microbe · 2026Review
- Higher Diagnostic Accuracy of an AI Model for Colposcopy Compared With Conventional and Digital Colposcopic Evaluation.Journal of lower genital tract disease · 2026Article
- Reinforcement learning-based WSI diagnostic system for cervical squamous cell carcinoma and precancerous lesions.Journal of translational medicine · 2026Article
- RE: An observational study of deep learning and automated evaluation of cervical images for cancer screening.Journal of the National Cancer Institute · 2026Article
- Visual diagnostics for female genital schistosomiasis and the opportunity for improvement using computer vision.Parasitology · 2025Review
- Initial evaluation of a new cervical screening strategy combining human papillomavirus genotyping and automated visual evaluation: the Human Papillomavirus-Automated Visual Evaluation Consortium.Journal of the National Cancer Institute · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides.Scientific reports · 2025Article
- Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer.Tomography (Ann Arbor, Mich.) · 2025Article
- Artificial Intelligence in Relation to Accurate Information and Tasks in Gynecologic Oncology and Clinical Medicine-Dunning-Kruger Effects and Ultracrepidarianism.Diagnostics (Basel, Switzerland) · 2025Review
- Generalizable deep neural networks for image quality classification of cervical images.Scientific reports · 2025Article
- Automated Image Clarity Detection for the Improvement of Colposcopy Imaging with Multiple Devices.Biomedical signal processing and control · 2025Article
- Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and barriers to clinical implementation.Frontiers in oncology · 2025Review
- Prospective Applications of Artificial Intelligence In Fetal Medicine: A Scoping Review of Recent Updates.International journal of general medicine · 2025Review
- Design and validation of ultra-compact metamaterial-based biosensor for non-invasive cervical cancer diagnosis in terahertz regime.PloS one · 2025Article
- A cervical cancer control strategy for lower-resource settings: interventions to complement one-dose HPV vaccination.Journal of the National Cancer Institute. Monographs · 2024Article
- Assessing generalizability of an AI-based visual test for cervical cancer screening.PLOS digital health · 2024Article
- Global burden of gynaecological cancers in 2022 and projections to 2050.Journal of global health · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Novel screening and diagnostic tests based on artificial intelligence (AI) image recognition algorithms are proliferating. Some initial reports claim outstanding accuracy followed by disappointing lack of confirmation, including our own early work on cervical screening. This is a presentation of lessons learned, organized as a conceptual step-by-step approach to bridge the gap between the creation of an AI algorithm and clinical efficacy. The first fundamental principle is specifying rigorously what the algorithm is designed to identify and what the test is intended to measure (eg, screening, diagnostic, or prognostic). Second, designing the AI algorithm to minimize the most clinically important errors. For example, many equivocal cervical images cannot yet be labeled because the borderline between cases and controls is blurred. To avoid a misclassified case-control dichotomy, we have isolated the equivocal cases and formally included an intermediate, indeterminate class (severity order of classes: case>indeterminate>control). The third principle is evaluating AI algorithms like any other test, using clinical epidemiologic criteria. Repeatability of the algorithm at the borderline, for indeterminate images, has proven extremely informative. Distinguishing between internal and external validation is also essential. Linking the AI algorithm results to clinical risk estimation is the fourth principle. Absolute risk (not relative) is the critical metric for translating a test result into clinical use. Finally, generating risk-based guidelines for clinical use that match local resources and priorities is the last principle in our approach. We are particularly interested in applications to lower-resource settings to address health disparities. We note that similar principles apply to other domains of AI-based image analysis for medical diagnostic testing.
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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.