Advances and Prospects of Artificial Intelligence in the Diagnosis and Management of Ovarian Cancer
- Authors
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Samar M. Almehdi
Iraqi Cancer Board, Ministry of Health, Baghdad, Iraq
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Rand Q. Al-Obaidi
Al-Yarmouk Teaching Hospital, Baghdad, Iraq
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- Keywords:
- Ovarian cancer, Artificial Intelligence, Machine Learning, Deep Learning, Precision oncology
- Abstract
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Ovarian cancer (OC), the most fatal gynecologic malignancy worldwide, is predicted to show rising trends in both incidence and mortality over the coming years. Late-stage presentation, limitations in currently available therapeutic approaches, and poor response in advanced disease are major contributors to unfavorable outcomes in these patients. Artificial intelligence (AI), in its various forms, has evolved in recent years into a revolutionary strategy with remarkable potential across different aspects of OC management. This narrative review is based on recently published peer-reviewed studies (2019–2025) identified through PubMed, Scopus, and Web of Science, with a focus on the diagnostic, prognostic, and therapeutic applications of AI in ovarian cancer. Studies suggest that AI models can equal, and in many cases surpass, human experts’ capabilities in cancer diagnosis and characterization. Medical image analysis using machine learning (ML) algorithms has shown exceptional performance, while deep learning (DL) models have been able to discriminate benign from malignant ovarian tumors with notable accuracy. Furthermore, AI-derived biomarkers provide noninvasive tools for screening and prognostication. Natural language processing (NLP) extracts valuable insights from clinical data to predict treatment outcomes, while ML integrates multi-omics data to optimize therapeutic regimens and surgical outcomes. AI models can also predict drug resistance, assist in identifying therapeutic targets, and help optimize treatment combinations. This comprehensive integration of AI technologies promises to transform OC care from late-stage intervention to early detection, precision prognostication, and personalized treatment strategies, offering renewed hope for improving patient outcomes in this challenging disease.
- References
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Copyright (c) 2026 Jehan M. Al-Musawi, Samar M. Almehdi, Sura A. Majeed, Rand Q. Al-Obaidi

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