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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 comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
Xiaoxuan Liu, Livia Faes, Aditya U. Kale, et al.
The Lancet Digital Health (2019) Vol. 1, Iss. 6, pp. e271-e297
Open Access | Times Cited: 1386

Showing 1-25 of 1386 citing articles:

AI in health and medicine
Pranav Rajpurkar, Emma Chen, Oishi Banerjee, et al.
Nature Medicine (2022) Vol. 28, Iss. 1, pp. 31-38
Closed Access | Times Cited: 1395
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
Ying Song, Shuangjia Zheng, Liang Li, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 18, Iss. 6, pp. 2775-2780
Open Access | Times Cited: 787
Preparing Medical Imaging Data for Machine Learning
Martin J. Willemink, Wojciech A. Koszek, Cailin Hardell, et al.
Radiology (2020) Vol. 295, Iss. 1, pp. 4-15
Open Access | Times Cited: 718
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
Xiaoxuan Liu, Samantha Cruz Rivera, David Moher, et al.
Nature Medicine (2020) Vol. 26, Iss. 9, pp. 1364-1374
Open Access | Times Cited: 609
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Ravi Aggarwal, Viknesh Sounderajah, Guy Martin, et al.
npj Digital Medicine (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 554
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
Gary S. Collins, Paula Dhiman, Constanza L. Andaur Navarro, et al.
BMJ Open (2021) Vol. 11, Iss. 7, pp. e048008-e048008
Open Access | Times Cited: 533
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
Lucas M. Fleuren, Thomas Klausch, Charlotte Zwager, et al.
Intensive Care Medicine (2020) Vol. 46, Iss. 3, pp. 383-400
Open Access | Times Cited: 486
Artificial Intelligence in Medicine: Today and Tomorrow
Giovanni Briganti, Olivier Le Moine
Frontiers in Medicine (2020) Vol. 7
Open Access | Times Cited: 461
Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist
Jean‐Paul Salameh, Patrick M. Bossuyt, Trevor A. McGrath, et al.
BMJ (2020), pp. m2632-m2632
Open Access | Times Cited: 451
How Machine Learning Will Transform Biomedicine
Jeremy Goecks, Vahid Jalili, Laura M. Heiser, et al.
Cell (2020) Vol. 181, Iss. 1, pp. 92-101
Open Access | Times Cited: 408
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
Samantha Cruz Rivera, Xiaoxuan Liu, An‐Wen Chan, et al.
Nature Medicine (2020) Vol. 26, Iss. 9, pp. 1351-1363
Open Access | Times Cited: 403
Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study
Hyo-Eun Kim, Hak Hee Kim, Boo‐Kyung Han, et al.
The Lancet Digital Health (2020) Vol. 2, Iss. 3, pp. e138-e148
Open Access | Times Cited: 380
Machine learning for medical imaging: methodological failures and recommendations for the future
Gaël Varoquaux, Veronika Cheplygina
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 370
Machine learning prediction in cardiovascular diseases: a meta-analysis
Chayakrit Krittanawong, Hafeez Ul Hassan Virk, Sripal Bangalore, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 335
Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review
Lu Xu, Leslie Sanders, Kay Li, et al.
JMIR Cancer (2021) Vol. 7, Iss. 4, pp. e27850-e27850
Open Access | Times Cited: 334
MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification
Jiancheng Yang, Rui Shi, Donglai Wei, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 332
Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities
Manu Goyal, Thomas Knackstedt, Shaofeng Yan, et al.
Computers in Biology and Medicine (2020) Vol. 127, pp. 104065-104065
Open Access | Times Cited: 313
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension
Xiaoxuan Liu, Samantha Cruz Rivera, David Moher, et al.
BMJ (2020), pp. m3164-m3164
Open Access | Times Cited: 283
A foundation model for generalizable disease detection from retinal images
Yukun Zhou, Mark A. Chia, Siegfried Wagner, et al.
Nature (2023) Vol. 622, Iss. 7981, pp. 156-163
Open Access | Times Cited: 283
Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs
Dan Miléa, Raymond P. Najjar, Zhubo Jiang, et al.
New England Journal of Medicine (2020) Vol. 382, Iss. 18, pp. 1687-1695
Open Access | Times Cited: 282
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare
Yuri Yin‐Moe Aung, David Wong, Daniel Shu Wei Ting
British Medical Bulletin (2021) Vol. 139, Iss. 1, pp. 4-15
Closed Access | Times Cited: 269
Artificial intelligence and machine learning for medical imaging: A technology review
Ana María Barragán Montero, Umair Javaid, Gilmer Valdés, et al.
Physica Medica (2021) Vol. 83, pp. 242-256
Open Access | Times Cited: 264
The Lancet Commission on diagnostics: transforming access to diagnostics
K A Fleming, Susan Horton, Michael L. Wilson, et al.
The Lancet (2021) Vol. 398, Iss. 10315, pp. 1997-2050
Open Access | Times Cited: 261
Designing deep learning studies in cancer diagnostics
Andreas Kleppe, Ole-Johan Skrede, Sepp de Raedt, et al.
Nature reviews. Cancer (2021) Vol. 21, Iss. 3, pp. 199-211
Closed Access | Times Cited: 257
Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review
Jiamin Yin, Kee Yuan Ngiam, Hock‐Hai Teo
Journal of Medical Internet Research (2021) Vol. 23, Iss. 4, pp. e25759-e25759
Open Access | Times Cited: 252
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