Ovarian cancer remains a highly lethal malignancy, with advanced-stage diagnosis, recurrence, and chemoresistance, thus limiting clinical outcomes. Traditional biomarkers such as CA-125, BRCA1/2 status, and histopathology offer only a partial view of disease biology, often leading to suboptimal and empiric treatment choices. Recent advances in artificial intelligence (AI) and machine learning (ML) provide new opportunities to improve diagnosis, risk stratification, therapeutic selection, and prevention. By integrating multimodal data, including imaging, clinical records, and multi-omics profiles, AI/ML models can uncover complex patterns that enhance the prediction of treatment response, toxicity, recurrence, and survival. Radiomics and radiomics-based prognostic value (RPV/eRPV) models add further precision by extracting informative imaging phenotypes. Emerging architectures such as graph neural networks (GNNs) and transformer-based models extend these capabilities by modeling interactions among genetic alterations, pathways, and drug responses. Beyond disease management, AI-driven risk prediction and screening tools are gaining exciting relevance in preventive oncology. This review summarizes current and developing AI/ML applications across ovarian cancer care and highlights the translational challenges and opportunities for integrating explainable AI into the clinical workflows. Collectively, these recent innovations support a more personalized, data-integrated approach to reducing morbidity and improving patient outcomes.
Singh et al. (Wed,) studied this question.