Artificial intelligence (AI) is rapidly reshaping gynecologic oncology across the continuum of care. This clinician-focused review synthesizes current evidence for AI-enabled prevention and screening (HPV-informed risk models, AI-assisted colposcopy), early detection and diagnosis (radiomics, liquid biopsy, and digital pathology), prognosis and risk prediction (multimodal models integrating clinical, imaging, histology, and genomics), and treatment guidance (surgical planning and response-predictive therapeutics). Across domains, deep learning and emerging multimodal models consistently match or surpass conventional approaches, offering gains in accuracy, speed, and reproducibility while enabling biologically informed decision support. We outline practical pathways for clinical integration, human-in-the-loop workflows, explainable outputs, and ethical and regulatory guardrails. Priority future directions include rigorous prospective trials, real-world performance tracking, and equity-centered deployment to ensure benefits generalize across diverse populations. Taken together, AI has the potential to enhance precision, consistency, and access in gynecologic cancer care, not by replacing clinicians, but by augmenting expertise at scale.
Keywords: artificial intelligence; deep learning; gynecologic oncology; machine learning; multimodal integration.
Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.