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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 in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis
Sian Mitchell, Manolis Nikolopoulos, AbdElhamid Elzarka, et al.
Cancers (2024) Vol. 16, Iss. 2, pp. 422-422
Open Access | Times Cited: 12

Showing 12 citing articles:

Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook
Bassam Abdul Rasool Hassan, Ali Haider Mohammed, Souheil Hallit, et al.
Frontiers in Oncology (2025) Vol. 15
Open Access | Times Cited: 2
Infertility and risk of ovarian cancer: A systematic review and meta analysis
Indra Sambasivam, H Gladius Jennifer, Saravanan Chinnaiyan
Journal of Family Medicine and Primary Care (2025) Vol. 14, Iss. 1, pp. 15-21
Open Access
An overview of the use of cutting-edge artificial intelligence (AI) modeling to produce synthetic medical data (SMD) in decentralized clinical machine learning (ML) for ovarian cancer(OC) and ovarian lymphoma(OL)
Diana Donatello
Journal of Ultrasound (2025)
Closed 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 in Relation to Accurate Information and Tasks in Gynecologic Oncology and Clinical Medicine—Dunning–Kruger Effects and Ultracrepidarianism
Edward J. Pavlik, Jason Woodward, Frank Lawton, et al.
Diagnostics (2025) Vol. 15, Iss. 6, pp. 735-735
Open Access
A nomogram combining clinical features, O-RADS US, and radiomics based on ultrasound imaging for diagnosing ovarian cancer
Wenting Xie, Yaoqin Wang, Zhongshi Du, et al.
Research Square (Research Square) (2025)
Closed 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
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
Ovarian Cancer Detection in Ascites Cytology with Weakly Supervised Model on Nationwide Dataset
J. Lee, Seung‐Ho Choi, Seoyeon Shin, et al.
American Journal Of Pathology (2025)
Closed Access
Designing Robots to Help Women
Martin Cooney, Lena M Widin Klasén, Fernando Alonso‐Fernandez
Linköping electronic conference proceedings (2024)
Open Access | Times Cited: 1
Intelligent system based on multiple networks for accurate ovarian tumor semantic segmentation
Mohamed El-Khatib, Dan Popescu, Oana Mihaela Teodor, et al.
Heliyon (2024) Vol. 10, Iss. 17, pp. e37386-e37386
Open Access | Times Cited: 1
The Role of Three-dimensional Power Doppler for Detecting Ovarian Cancer in Adnexal Masses: A Systematic Review and Meta-analysis
E. Sánchez, Alba Verdú, A. Carbonell, et al.
Deleted Journal (2024) Vol. 2, Iss. 1, pp. 18-28
Open Access
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