Precision Medicine in Ovarian, Cervical, and Endometrial Cancer Using Artificial Intelligence

Abstract

Background: Precision medicine has transformed the management of gynecologic malignancies by enabling individualized diagnosis, prognostic assessment, therapeutic selection, and long-term disease monitoring based on each patient's unique molecular and clinical characteristics. Ovarian, cervical, and endometrial cancers exhibit remarkable biological heterogeneity arising from distinct genomic alterations, epigenetic modifications, immune interactions, hormonal influences, and tumor microenvironment dynamics. Conventional treatment strategies based primarily on histopathological classification and population-level clinical evidence frequently fail to capture this complexity, resulting in considerable variability in therapeutic response and patient outcomes. Recent advances in artificial intelligence (AI), multimodal learning, computational pathology, radiomics, radiogenomics, and molecular oncology have substantially enhanced precision medicine by integrating medical imaging, genomic sequencing, transcriptomics, proteomics, digital pathology, laboratory investigations, electronic health records, and longitudinal clinical information into comprehensive computational models. Machine learning, deep learning, transformer architectures, graph neural networks, foundation models, reinforcement learning, and explainable artificial intelligence now support early cancer detection, molecular classification, treatment optimization, prediction of therapeutic response, recurrence monitoring, and survivorship care across gynecologic oncology. Despite remarkable progress, challenges remain regarding data interoperability, model transparency, ethical governance, cybersecurity, regulatory approval, and equitable clinical implementation. This review provides a comprehensive overview of artificial intelligence-driven precision medicine in ovarian, cervical, and endometrial cancer, highlighting computational foundations, clinical applications, emerging technologies, and future perspectives for personalized gynecologic oncology.