ReviewCureus2026
Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical, endometrial, ovarian, vulvar, vaginal, fallopian tube, and gestational trophoblastic neoplasia (GTN) are major gynecologic cancers that significantly impact women's health globally. In spite of progress in surgery, chemotherapy, radiotherapy, and targeted therapies, results are still variable, and timely diagnosis frequently proves challenging. Artificial intelligence (AI) has progressively taken advantage of, in digital pathological conditions risk prognostication for gynecological pathologies like endometrial and ovarian cancers, and automated Pap smear clarification for cervical cancer. Multi-platform methods combining clinical approaches and imaging data may help with prognostic assessments and personalized therapies. Nevertheless, major clinical information arises from single-center retrospective studies with minimal external authentication, and challenges like data heterogeneity, the lack of systematized protocols, ethical concerns, algorithmic bias, transparency, and workflow integration must be addressed in light of wide-ranging clinical and scientific approval.
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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.