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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:

A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions
Pallabi Shrestha, Bhavya Poudyal, Sepideh Yadollahi, et al.
Gynecologic Oncology (2022) Vol. 166, Iss. 3, pp. 596-605
Closed Access | Times Cited: 52

Showing 1-25 of 52 citing articles:

Exploring data mining and machine learning in gynecologic oncology
Ferdaous Idlahcen, Ali Idri, Evgin Göçeri
Artificial Intelligence Review (2024) Vol. 57, Iss. 2
Open Access | Times Cited: 24
Artificial intelligence in ovarian cancer histopathology: a systematic review
Jack Breen, Katrina J. Allen, Kieran Zucker, et al.
npj Precision Oncology (2023) Vol. 7, Iss. 1
Open Access | Times Cited: 35
Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors
Ya‐Ting Jan, Pei‐Shan Tsai, Wenhui Huang, et al.
Insights into Imaging (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 30
Role of artificial intelligence in digital pathology for gynecological cancers
Yali Wang, Song Gao, Qian Xiao, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 24, pp. 205-212
Open Access | Times Cited: 15
Global burden of gynaecological cancers in 2022 and projections to 2050
Binhua Zhu, Hao Gu, Zhihan Mao, et al.
Journal of Global Health (2024) Vol. 14
Open Access | Times Cited: 14
Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)
Yanli Wang, Weihong Lin, Xiaoling Zhuang, et al.
Oncology Reports (2024) Vol. 51, Iss. 3
Open Access | Times Cited: 13
Diagnostic Accuracy of Transvaginal Ultrasound and Magnetic Resonance Imaging for the Detection of Myometrial Infiltration in Endometrial Cancer: A Systematic Review and Meta-Analysis
I. Madár, Anett Szabó, Gábor Vleskó, et al.
Cancers (2024) Vol. 16, Iss. 5, pp. 907-907
Open Access | Times Cited: 10
Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review
F. Moro, Maria Teresa Giudice, Mariano Ciancia, et al.
Ultrasound in Obstetrics and Gynecology (2025)
Open Access | Times Cited: 1
Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis
Yan Li, Li Q, Kang Fu, et al.
Bioengineering (2025) Vol. 12, Iss. 3, pp. 288-288
Open Access | Times Cited: 1
Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990–2022)
Joshua Sheehy, Hamish Rutledge, U. Rajendra Acharya, et al.
Artificial Intelligence in Medicine (2023) Vol. 139, pp. 102536-102536
Closed Access | Times Cited: 19
Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review
F. Moro, Marianna Ciancia, Drieda Zaçe, et al.
International Journal of Cancer (2024) Vol. 155, Iss. 10, pp. 1832-1845
Open Access | Times Cited: 8
Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis
Qingyi Wang, Zhuo Chang, X. Liu, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e48527-e48527
Open Access | Times Cited: 7
Radiomics systematic review in cervical cancer: gynecological oncologists’ perspective
Nicolò Bizzarri, Luca Russo, Miriam Dolciami, et al.
International Journal of Gynecological Cancer (2023) Vol. 33, Iss. 10, pp. 1522-1541
Closed Access | Times Cited: 15
Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis
Meng-Lin Huang, Jing Ren, Zhengyu Jin, et al.
La radiologia medica (2024) Vol. 129, Iss. 3, pp. 439-456
Closed Access | Times Cited: 6
Comprehensive Review of Uterine Leiomyosarcoma: Pathogenesis, Diagnosis, Prognosis, and Targeted Therapy
Qiwei Yang, Obianuju Sandra Madueke-Laveaux, Han Cun, et al.
Cells (2024) Vol. 13, Iss. 13, pp. 1106-1106
Open Access | Times Cited: 6
Revolutionizing Women’s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology
Marta Brandão, Francisco Mendes, Miguel Martins, et al.
Journal of Clinical Medicine (2024) Vol. 13, Iss. 4, pp. 1061-1061
Open Access | Times Cited: 5
Adopting Artificial Intelligence in Healthcare
Kamel Mouloudj, Vu Lan Oanh LE, Achouak Bouarar, et al.
Advances in marketing, customer relationship management, and e-services book series (2023), pp. 1-20
Closed Access | Times Cited: 13
Role of MRI in the Assessment of Cervical Cancer
Giuseppe Lo Re, Giuseppe Cucinella, Giulia Zaccaria, et al.
Seminars in Ultrasound CT and MRI (2023) Vol. 44, Iss. 3, pp. 228-237
Closed Access | Times Cited: 12
Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers
Pankaj Garg, Atish Mohanty, Sravani Ramisetty, et al.
Biochimica et Biophysica Acta (BBA) - Reviews on Cancer (2023) Vol. 1878, Iss. 6, pp. 189026-189026
Open Access | Times Cited: 12
Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools
Luca Russo, Silvia Bottazzi, Burak Koçak, et al.
European Radiology (2024) Vol. 35, Iss. 1, pp. 202-214
Open Access | Times Cited: 4
Artificial intelligence model for enhancing the accuracy of transvaginal ultrasound in detecting endometrial cancer and endometrial atypical hyperplasia
Ilaria Capasso, Giuseppe Cucinella, Darryl Wright, et al.
International Journal of Gynecological Cancer (2024) Vol. 34, Iss. 10, pp. 1547-1555
Closed Access | Times Cited: 4
The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects
Miguel Oliveira, Francisco Mendes, Miguel Martins, et al.
Diagnostics (2025) Vol. 15, Iss. 3, pp. 274-274
Open Access
Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach
Guoyuan Pan, Zejun Pan, Wubiao Chen, et al.
Magnetic Resonance Imaging (2025), pp. 110339-110339
Closed Access
Opportunistic prognostication by computerized tomography (CT) in the emergency department: analysis on 1920 patients and creation of a simple and fast scoring system
Alberto Tagliafico, Stefano Benenati, Italo Porto, et al.
La radiologia medica (2025)
Closed Access
Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis
Jiang Junjie, Zeyu Wang, Bowei Zhao, et al.
Journal of Medical Internet Research (2025) Vol. 27, pp. e66530-e66530
Open Access
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