ArticleNature communications2024
Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
60 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools.Cancer cytopathology · 2026Pooled it
- A bibliometric analysis and visualization of trends in cervical cancer screening technologies based on dual-database analysis.Frontiers in oncology · 2026Pooled it
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- A universal visual foundation model for computational cytopathology.Nature cancer · 2026Article
- Review
- Accurate Segmentation of Overlapping Cervical Cells Using an Optimized Deep Learning Framework for Cytology Screening.Diagnostics (Basel, Switzerland) · 2026Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- The Designated Institutional Official: An Essential GME Role, But What Do They Actually Do?Journal of general internal medicine · 2026Article
- Biology-aligned cervical cancer screening: target-centric biomarkers and next-generation diagnostic platforms.Biomarker research · 2026Review
- Deep learning-based cervical cancer T-staging using MRI: multi-structure segmentation and classification.BMC medical imaging · 2026Article
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- MurineCyto-Det: A High-Resolution Murine BALF Cytology Dataset for Leukocyte Segmentation and Detection.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Deep learning-assisted versus manual reading in routine cervical cytopathology: a multicentre randomised crossover trial.NPJ digital medicine · 2026Article
- Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Therapeutic Landscape of HPV-Associated Cancers: From Mechanisms and Conventional Approaches to Future Innovations.Cancers · 2026Review
- Quantitative Texture Analysis of Cervical Cytology Identifies Endometrial Lesions in Atypical Glandular Cells on Liquid-Based Cytology: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.
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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.