ReviewFrontiers in oncology2026
Artificial intelligence in ovarian cancer prevention and control: a brief review of detection, treatment, and equity (2021-2026).
Review in Frontiers in oncology, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer (OC) remains a leading cause of gynecologic cancer mortality worldwide because early symptoms are often vague, effective population-level screening strategies are lacking, and outcomes are shaped by biological heterogeneity and unequal access to diagnostic and molecular testing resources. This Mini Review summarizes advances from 2021 to 2026 in artificial intelligence (AI) applications across the OC care continuum, focusing on early detection, treatment decision-making, prognostic assessment, clinical implementation, and health equity. This review maps AI applications against current clinical limitations, established benchmark tools, translational readiness, and equity-related implementation gaps. In detection, AI models integrate multimarker blood panels, routine laboratory indicators, ultrasound imaging, and cell-free DNA (cfDNA)-derived molecular signals to improve risk stratification beyond traditional biomarkers such as CA125, although prospective validation remains limited. In treatment and prognosis, pathology-based, radiomics-based, omics-based, and multimodal AI models show potential for predicting homologous recombination deficiency (HRD), platinum response, recurrence risk, and survival outcomes, but most remain retrospective or externally validated rather than clinically deployed. Blood/laboratory-based and ultrasound-assisted AI appear closest to workflow-based evaluation, whereas cfDNA-based screening, population-level AI detection, and multimodal prognostic models remain preliminary. Future efforts should prioritize prospective multicenter studies, benchmarked evaluation, transparent reporting, cost-effectiveness analysis, workflow integration, and globally representative datasets. Ultimately, AI should be judged by its ability to improve timely, affordable, and equitable OC care.
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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 Socratic graph.