Volume 17, Issue 2 (June-2025 2025)                   Iranian Journal of Blood and Cancer 2025, 17(2): 46-57 | Back to browse issues page

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Rahimi M, Kashani K, Hosseinpour V, Gozali E. Artificial Intelligence in Gynecologic Cancer: A Review of Applications and Advancements. Iranian Journal of Blood and Cancer 2025; 17 (2) :46-57
URL: http://ijbc.ir/article-1-1741-en.html
Artificial Intelligence in Gynecologic Cancer: A Review of Applications and Advancements
Milad Rahimi1 , Kasra Kashani1 , Vahid Hosseinpour2 , Elahe Gozali *3
1- Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran. & Department of Medical Informatics, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran.
2- Department of Emergency Medicine, Urmia University of Medical Sciences, Urmia, Iran.
3- 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. , gozali_e@umsu.ac.ir
Abstract:   (223 Views)
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.
Keywords: Gynecologic cancers, Oncology, Malignancy, Artificial intelligence, Deep learning, Machine learning
Full-Text [PDF 467 kb]   (112 Downloads)    
: Review Article | Subject: AI in Medicine
Received: 2025/05/7 | Accepted: 2025/05/29 | Published: 2025/06/30
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