@article{Lin2025, 
author = {Zijing Lin and Jinwei Qiang and Yajia Gu and Haiming Li},
title = {Artificial Intelligence in Ovarian Cancer: Current Advances and Perspectives},
year = {2025},
journal = {Medicine Advances},
volume = {3},
number = {4},
pages = {256-267},
keywords = {artificial intelligence, deep learning, female ovarian neoplasm, genital neoplasm, radiomics},
url = {https://www.sciopen.com/article/10.1002/med4.70036},
doi = {10.1002/med4.70036},
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.}
}