en AI in Medicine مقاله مروری Review Article <div style="text-align: justify;"><span style="line-height:2;"><span style="font-family:Times New Roman;"><span style="font-size:14px;"><span new="" roman="" times="">Gynecologic cancers, including cervical, ovarian, endometrial, vaginal, and vulvar malignancies, remain a major global health burden, accounting for substantial morbidity and mortality among women. Despite advances in conventional treatments such as surgery, chemotherapy, and radiotherapy, survival outcomes remain suboptimal, particularly in cases diagnosed at advanced stages. In recent years, artificial intelligence (AI) has emerged as a transformative tool in gynecologic oncology, offering novel approaches to enhance diagnostic accuracy, stratify risk, personalize treatment strategies, and streamline clinical workflows. This narrative review provides a comprehensive overview of the current and emerging applications of AI in the management of gynecologic cancers. Key developments are discussed, including deep learning models for imaging interpretation, AI-driven biomarker analysis for early detection, and predictive algorithms for assessing treatment response and toxicity risk. Additionally, the use of AI in automating cytopathology and optimizing resource allocation is explored. While early findings are promising, challenges remain regarding the generalizability of AI models across diverse populations, the need for standardized datasets, and the integration of AI tools into routine clinical practice. Addressing these limitations is essential to ensure safe, equitable, and effective implementation. Overall, this review underscores the potential of AI to significantly improve patient outcomes and clinical efficiency in gynecologic oncology. Future research and interdisciplinary collaboration will be critical in translating these innovations into real-world clinical benefit.</span></span></span></span></div> Gynecologic cancers, Oncology, Malignancy, Artificial intelligence, Deep learning, Machine learning 46 57 http://ijbc.ir/browse.php?a_code=A-10-1990-1&slc_lang=en&sid=1 Milad Rahimi 100319475328460012172 100319475328460012172 No Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran. Kasra Kashani 100319475328460012173 100319475328460012173 No Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran. Vahid Hosseinpour 100319475328460012174 100319475328460012174 No Department of Emergency Medicine, Urmia University of Medical Sciences, Urmia, Iran. Elahe Gozali gozali_e@umsu.ac.ir 100319475328460012175 100319475328460012175 Yes Assistant Professor, Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran. Corresponding Author. ORCID: https://orcid.org/0000-0002-9211-5934. Email: gozali_e@umsu.ac.ir.