Artificial Intelligence in Ovarian Cancer: Current Advances and Perspectives

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.

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Publication Details

Journal
Medicine Advances
Published
2025-11-26
DOI
https://doi.org/10.1002/med4.70036
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Artificial Intelligence in Ovarian Cancer: Current Advances and Perspectives

Zijing Lin, Yajia Gu, Jinwei Qiang, Haiming Li
Medicine Advances
Radiomics and Machine Learning in Medical Imaging
article

Artificial Intelligence in Ovarian Cancer: Current Advances and Perspectives

Zijing Lin, Yajia Gu, Jinwei Qiang, Haiming Li
article en

Abstract

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.

Medicine AdvancesVol. 3(4)
Fudan University Shanghai Cancer Center (CN), Jinshan Hospital of Fudan University (CN)
Shanghai Anti-Cancer Association
Openalex Percentile: Top 48%
Radiomics and Machine Learning in Medical Imaging
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Artificial Intelligence in Ovarian Cancer: Current Advances and Perspectives — Zijing Lin, Yajia Gu, et al. · Medicine Advances (2025) | TGRS Research Map | TGRS