ArticleMolecular and clinical oncology2022
An artificial intelligence-assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions.
Article in Molecular and clinical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 24 citations in OpenAlex.
- Evaluation of ChatGPT-5-generated clinical management recommendations for HPV infection and cervical premalignant lesions: a scenario-based expert assessment study.BMC medical informatics and decision making · 2026Article
- Integrating Biomarkers into Cervical Cancer Screening-Advances in Diagnosis and Risk Prediction: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Advances in artificial intelligence for the early detection of cervical cancer in adult women: a scoping review.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2025Article
- Artificial intelligence strengthens cervical cancer screening - present and future.Cancer biology & medicine · 2024Review
- Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching.BMC medical education · 2024Article
- High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt.Bioengineering (Basel, Switzerland) · 2023Article
- Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features.Diagnostics (Basel, Switzerland) · 2023Article
- Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.Cancer medicine · 2023Article
- Artificial intelligence in colposcopic examination: A promising tool to assist junior colposcopists.Frontiers in medicine · 2023Article
- Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis.Journal of gynecologic oncology · 2022Article
- Diagnostic Efficacy of Enhanced Visual Assessment [Visual Check] for Triaging Cervical Cancer Screen Positive Women.Journal of mid-life healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study created an artificial intelligence (AI)-automated diagnostics system for uterine cervical lesions and assessed the performance of these images for AI diagnostic imaging of pathological cervical lesions. A total of 463 colposcopic images were analyzed. The traditional colposcopy diagnoses were compared to those obtained by AI image diagnosis. Next, 100 images were presented to a panel of 32 gynecologists who independently examined each image in a blinded fashion and diagnosed them for four categories of tumors. Then, the 32 gynecologists revisited their diagnosis for each image after being informed of the AI diagnosis. The present study assessed any changes in physician diagnosis and the accuracy of AI-image-assisted diagnosis (AISD). The accuracy of AI was 57.8% for normal, 35.4% for cervical intraepithelial neoplasia (CIN)1, 40.5% for CIN2-3 and 44.2% for invasive cancer. The accuracy of gynecologist diagnoses from cervical pathological images, before knowing the AI image diagnosis, was 54.4% for CIN2-3 and 38.9% for invasive cancer. After learning of the AISD, their accuracy improved to 58.0% for CIN2-3 and 48.5% for invasive cancer. AI-assisted image diagnosis was able to improve gynecologist diagnosis accuracy significantly (P<0.01) for invasive cancer and tended to improve their accuracy for CIN2-3 (P=0.14).
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.