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Review | Open Access

Artificial Intelligence in Ovarian Cancer: Current Advances and Perspectives

Zijing Lin1,2, Jinwei Qiang1, Yajia Gu2, Haiming Li2 ( )
Department of Radiology, Jinshan Hospital of Fudan University, Shanghai, China
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
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Abstract

Ovarian cancer is a commonly encountered gynecological malignancy and an important health problem in the female population worldwide. Artificial intelligence (AI) has been rapidly developing in oncology, with promising achievements in terms of facilitating personalized care and improving patient survival in women with ovarian cancer. This review summarizes the present application of AI‐based techniques in ovarian cancer, with a focus on radiological imaging. It provides detailed information on the use of radiomics and deep learning‐based models in the clinical management of this disease. It also discusses current challenges, including interpretability, generalizability, and ethical and regulatory considerations, as well as future perspectives regarding the clinical use of AI‐powered tools in ovarian cancer.

Graphical Abstract

This article provides a comprehensive summary of the applications of artificial intelligence, including radiomics and deep learning, in ovarian cancer. It also discusses the current challenges and future directions, which mainly involve certain aspects of model generalizability, interpretability, and ethical and regulatory considerations. The GA image of this work was created in BioRender. Lin, Z. (2025, https://BioRender.com/4ip6lh0).

Keywords

artificial intelligencedeep learningfemale ovarian neoplasmgenital neoplasmradiomics

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Medicine Advances
Volume 3 Issue 4,
December 2025
Pages 256-267
DOI: 10.1002/med4.70036

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Cite this article:
Lin Z, Qiang J, Gu Y, et al. Artificial Intelligence in Ovarian Cancer: Current Advances and Perspectives. Medicine Advances, 2025, 3(4): 256-267. https://doi.org/10.1002/med4.70036

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Received: 06 January 2025
Revised: 24 May 2025
Accepted: 27 May 2025
Published: 13 January 2026
© 2025 The Author(s). Tsinghua University Press.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.