Artificial intelligence in ovarian cancer prevention and control: a brief review of detection, treatment, and equity (2021-2026).

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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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