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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:

High-performance medicine: the convergence of human and artificial intelligence
Eric J. Topol
Nature Medicine (2018) Vol. 25, Iss. 1, pp. 44-56
Closed Access | Times Cited: 4765

Showing 1-25 of 4765 citing articles:

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, et al.
Journal Of Big Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 4715
Machine Learning in Medicine
Alvin Rajkomar, Jay B. Dean, Isaac S. Kohane
New England Journal of Medicine (2019) Vol. 380, Iss. 14, pp. 1347-1358
Closed Access | Times Cited: 2599
International evaluation of an AI system for breast cancer screening
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature (2020) Vol. 577, Iss. 7788, pp. 89-94
Open Access | Times Cited: 2300
Key challenges for delivering clinical impact with artificial intelligence
Christopher Kelly, Alan Karthikesalingam, Mustafa Suleyman, et al.
BMC Medicine (2019) Vol. 17, Iss. 1
Open Access | Times Cited: 1538
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Erico Tjoa, Cuntai Guan
IEEE Transactions on Neural Networks and Learning Systems (2020) Vol. 32, Iss. 11, pp. 4793-4813
Open Access | Times Cited: 1396
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
Big data and machine learning algorithms for health-care delivery
Kee Yuan Ngiam, Ing Wei Khor
The Lancet Oncology (2019) Vol. 20, Iss. 5, pp. e262-e273
Closed Access | Times Cited: 1089
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Shuroug A. Alowais, Sahar S. Alghamdi, Nada Alsuhebany, et al.
BMC Medical Education (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 1018
Precision Medicine, AI, and the Future of Personalized Health Care
Kevin B. Johnson, Wei‐Qi Wei, Dilhan Weeraratne, et al.
Clinical and Translational Science (2020) Vol. 14, Iss. 1, pp. 86-93
Open Access | Times Cited: 947
IL-6 in inflammation, autoimmunity and cancer
Toshio Hirano
International Immunology (2020) Vol. 33, Iss. 3, pp. 127-148
Open Access | Times Cited: 865
Liquid biopsy enters the clinic — implementation issues and future challenges
Michail Ignatiadis, George W. Sledge, Stefanie S. Jeffrey
Nature Reviews Clinical Oncology (2021) Vol. 18, Iss. 5, pp. 297-312
Closed Access | Times Cited: 855
Deep learning-enabled medical computer vision
Andre Esteva, Katherine Chou, Serena Yeung, et al.
npj Digital Medicine (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 850
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography
Kang Zhang, Xiaohong Liu, Jun Shen, et al.
Cell (2020) Vol. 181, Iss. 6, pp. 1423-1433.e11
Open Access | Times Cited: 828
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database
Stan Benjamens, Pranavsingh Dhunnoo, Bertalan Meskó
npj Digital Medicine (2020) Vol. 3, Iss. 1
Open Access | Times Cited: 823
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Sebastian Raisch, Sebastian Krakowski
Academy of Management Review (2021) Vol. 46, Iss. 1, pp. 192-210
Open Access | Times Cited: 815
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
Myura Nagendran, Yang Chen, Christopher A. Lovejoy, et al.
BMJ (2020), pp. m689-m689
Open Access | Times Cited: 795
Targeting Strategies for Tissue-Specific Drug Delivery
Zongmin Zhao, Anvay Ukidve, Jayoung Kim, et al.
Cell (2020) Vol. 181, Iss. 1, pp. 151-167
Open Access | Times Cited: 743
Vision, challenges, roles and research issues of Artificial Intelligence in Education
Gwo‐Jen Hwang, Haoran Xie, Benjamin W. Wah, et al.
Computers and Education Artificial Intelligence (2020) Vol. 1, pp. 100001-100001
Open Access | Times Cited: 740
The false hope of current approaches to explainable artificial intelligence in health care
Marzyeh Ghassemi, Luke Oakden‐Rayner, Andrew L. Beam
The Lancet Digital Health (2021) Vol. 3, Iss. 11, pp. e745-e750
Open Access | Times Cited: 724
Rethinking drug design in the artificial intelligence era
Petra Schneider, W. Patrick Walters, Alleyn T. Plowright, et al.
Nature Reviews Drug Discovery (2019) Vol. 19, Iss. 5, pp. 353-364
Open Access | Times Cited: 646
Artificial intelligence in healthcare: transforming the practice of medicine
Junaid Bajwa, Usman Munir, Aditya Nori, et al.
Future Healthcare Journal (2021) Vol. 8, Iss. 2, pp. e188-e194
Open Access | Times Cited: 630
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
Zeeshan Ahmed, Khalid Gaffer Mohamed, Saman Zeeshan, et al.
Database (2020) Vol. 2020
Open Access | Times Cited: 619
Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks
Todd Hollon, Balaji Pandian, Arjun Adapa, et al.
Nature Medicine (2020) Vol. 26, Iss. 1, pp. 52-58
Open Access | Times Cited: 596
Artificial Intelligence for Mental Health and Mental Illnesses: an Overview
Sarah Graham, Colin A. Depp, Ellen Lee, et al.
Current Psychiatry Reports (2019) Vol. 21, Iss. 11
Open Access | Times Cited: 591
Multimodal biomedical AI
Julián Acosta, Guido J. Falcone, Pranav Rajpurkar, et al.
Nature Medicine (2022) Vol. 28, Iss. 9, pp. 1773-1784
Open Access | Times Cited: 562
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