ReviewFrontiers in oncology2025
Prospects and challenges of deep learning in gynecologic malignancies.
Review in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Article
- Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives.Cureus · 2026Review
- Translational Assessment of Omics Approaches in Endometriosis: Bridging Molecular Discovery with Clinical Utility.International journal of molecular sciences · 2026Review
- Glycomic Insights in Gynecological Disease: From Molecular Mechanisms to Precision Diagnostics and Therapeutics.International journal of molecular sciences · 2026Review
- From static snapshots to longitudinal trajectories: artificial intelligence in women's reproductive and ovarian health.Frontiers in endocrinology · 2026Review
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
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is revolutionizing oncology, with deep learning (DL) emerging as a pivotal technology for addressing gynecologic malignancies (GMs). DL-based models are now widely applied to assist in clinical diagnosis and prognosis prediction, demonstrating excellent performance in tasks such as tumor detection, segmentation, classification, and necrosis assessment for both primary and metastatic GMs. By leveraging radiological (e.g., X-ray, CT, MRI, and Single Photon Emission Computed Tomography (SPECT)) and pathological images, these approaches show significant potential for enhancing diagnostic accuracy and prognostic evaluation. This review provides a concise overview of deep learning techniques for medical image analysis and their current applications in GM diagnosis and outcome prediction. Furthermore, it discusses key challenges and future directions in the field. AI-based radiomics presents a non-invasive and cost-effective tool for gynecologic practice, and the integration of multi-omics data is recommended to further advance precision medicine in oncology.
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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.