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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis
He-Li Xu, Tingting Gong, Fang-Hua Liu, et al.
EClinicalMedicine (2022) Vol. 53, pp. 101662-101662
Open Access | Times Cited: 83

Showing 26-50 of 83 citing articles:

Current Status and Prospect of the Diagnosis and Treatment Mode of Multidisciplinary Team in Ovarian Cancer
宁 刘
Advances in Clinical Medicine (2025) Vol. 15, Iss. 01, pp. 538-545
Closed Access
Early Diagnosis of Ovarian Cancer: A Comprehensive Review of the Advances, Challenges, and Future Directions
Mun-Kun Hong, Dah‐Ching Ding
Diagnostics (2025) Vol. 15, Iss. 4, pp. 406-406
Open Access
A Novel SHAP-GAN Network for Interpretable Ovarian Cancer Diagnosis
Jinhai Cai, Zne-Jung Lee, Zhouchen Lin, et al.
Mathematics (2025) Vol. 13, Iss. 5, pp. 882-882
Open Access
Evaluation of a novel ensemble model for preoperative ovarian cancer diagnosis: Clinical factors, O-RADS, and deep learning radiomics
Yimin Wu, Lifang Fan, Haixin Shao, et al.
Translational Oncology (2025) Vol. 54, pp. 102335-102335
Closed Access
Artificial Intelligence for Ovarian Cancer Detection with Medical Images: A Review of the Last Decade (2013–2023)
Amir Reza Naderi Yaghouti, Ahmad Shalbaf, Roohallah Alizadehsani, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access
Modern Emerging Biosensing Methodologies for the Early Diagnosis and Screening of Ovarian Cancer
Farah Abul Rub, Naseel Moursy, Nouf Alhedeithy, et al.
Biosensors (2025) Vol. 15, Iss. 4, pp. 203-203
Open Access
AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis
Haishan Xu, Xiao-Ying Li, Ming-Qian Jia, et al.
Journal of Medical Internet Research (2025) Vol. 27, pp. e67922-e67922
Open Access
Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews
Haishan Xu, Ting‐Ting Gong, Xin‐Jian Song, et al.
Journal of Medical Internet Research (2025) Vol. 27, pp. e53567-e53567
Open Access
Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography
MinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, et al.
PLOS Digital Health (2025) Vol. 4, Iss. 4, pp. e0000811-e0000811
Open Access
Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy
Mauro Francesco Pio Maiorano, Gennaro Cormio, Vera Loizzi, et al.
AI (2025) Vol. 6, Iss. 4, pp. 84-84
Open Access
Integrating Ultrasound-CT-MR for Preoperative Multi-Task Prediction in Ovarian Cancer: Achieving Diagnostic Parity with Multidisciplinary Team Consensus
Jingjing Yu, Peijun Hu, Ruixia Dai, et al.
Research Square (Research Square) (2025)
Closed Access
Challenges and Solutions in Securing AI Algorithms in Healthcare
Jabin Shiji Koshy
Advances in computational intelligence and robotics book series (2025), pp. 35-66
Closed Access
A pioneering artificial intelligence tool to predict treatment outcomes in ovarian cancer via diagnostic laparoscopy
Xiaotian Ma, Yu‐Chun Hsu, Amma Asare, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Artificial intelligence in the diagnosis and management of gynecologic cancer
Chaiyawut Paiboonborirak, Nadeem R. Abu‐Rustum, Sarikapan Wilailak
International Journal of Gynecology & Obstetrics (2025)
Open Access
Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024
Xian Zeng, Zhijun Li, Lei Dai, et al.
Discover Oncology (2025) Vol. 16, Iss. 1
Open Access
Visual-Language Foundation Models in Medical Imaging: A Systematic Review and Meta-Analysis of Diagnostic and Analytical Applications
Yaoyao Sun, Xinran Wen, Yan Zhang, et al.
Computer Methods and Programs in Biomedicine (2025), pp. 108870-108870
Closed Access
Artificial intelligence for HPV status prediction based on disease‐specific images in head and neck cancer: A systematic review and meta‐analysis
Cheng Song, Xu Chen, Chao Tang, et al.
Journal of Medical Virology (2023) Vol. 95, Iss. 9
Closed Access | Times Cited: 9
A Comprehensive Review of Screening Methods for Ovarian Masses: Towards Earlier Detection
Shreya A Sahu, Deepti Shrivastava
Cureus (2023)
Open Access | Times Cited: 9
Advancing Ovarian Cancer Diagnosis Through Deep Learning and eXplainable AI: A Multiclassification Approach
Meera Radhakrishnan, Niranjana Sampathila, H Muralikrishna, et al.
IEEE Access (2024) Vol. 12, pp. 116968-116986
Open Access | Times Cited: 3
Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer
Mingxiang Wei, Guannan Feng, Xinyi Wang, et al.
Academic Radiology (2023) Vol. 31, Iss. 6, pp. 2391-2401
Closed Access | Times Cited: 8
Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records
Hyejung Chang, Jae-Young Choi, Jaesun Shim, et al.
Healthcare Informatics Research (2023) Vol. 29, Iss. 4, pp. 323-333
Open Access | Times Cited: 7
Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review
Ahror Jabborov, Arina Kharlamova, Zamira Kholmatova, et al.
IEEE Access (2023) Vol. 11, pp. 130491-130507
Open Access | Times Cited: 7
Review of meta-analyses on the use of artificial intelligence in radiology
Yu.A. Vasilev, A. V. Vladzimirskyy, Olga V. Omelyanskaya, et al.
Medical Visualization (2024) Vol. 28, Iss. 3, pp. 22-41
Closed Access | Times Cited: 2
Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis
Xin Xing, Shanshan Wu, He-Li Xu, et al.
EClinicalMedicine (2024) Vol. 77, pp. 102897-102897
Open Access | Times Cited: 2
Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study
Wenli Dai, Yingnan Wu, Yating Ling, et al.
EClinicalMedicine (2024) Vol. 78, pp. 102923-102923
Open Access | Times Cited: 2

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