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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:

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

Showing 1-25 of 2599 citing articles:

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Jonathan Waring, Charlotta Lindvall, Renato Umeton
Artificial Intelligence in Medicine (2020) Vol. 104, pp. 101822-101822
Open Access | Times Cited: 668
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
Swarm Learning for decentralized and confidential clinical machine learning
Stefanie Warnat‐Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, et al.
Nature (2021) Vol. 594, Iss. 7862, pp. 265-270
Open Access | Times Cited: 597
An Introduction to Machine Learning
Solveig Badillo, Balázs Bánfai, Fabian Birzele, et al.
Clinical Pharmacology & Therapeutics (2020) Vol. 107, Iss. 4, pp. 871-885
Open Access | Times Cited: 551
AI applications to medical images: From machine learning to deep learning
Isabella Castiglioni, Leonardo Rundo, Marina Codari, et al.
Physica Medica (2021) Vol. 83, pp. 9-24
Open Access | Times Cited: 483
The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
Melissa Haendel, Christopher G. Chute, Tellen D. Bennett, et al.
Journal of the American Medical Informatics Association (2020) Vol. 28, Iss. 3, pp. 427-443
Open Access | Times Cited: 457
Gender equality in science, medicine, and global health: where are we at and why does it matter?
Geordan Shannon, Melanie Jansen, Kate Williams, et al.
The Lancet (2019) Vol. 393, Iss. 10171, pp. 560-569
Open Access | Times Cited: 454
Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) Guideline
Michał F. Kamiński, Cesare Hassan, Raf Bisschops, et al.
Endoscopy (2014) Vol. 46, Iss. 05, pp. 435-457
Open Access | Times Cited: 414
The Clinician and Dataset Shift in Artificial Intelligence
Samuel G. Finlayson, Adarsh Subbaswamy, Karandeep Singh, et al.
New England Journal of Medicine (2021) Vol. 385, Iss. 3, pp. 283-286
Open Access | Times Cited: 409
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
Significance of machine learning in healthcare: Features, pillars and applications
Mohd Javaid, Abid Haleem, Ravi Pratap Singh, et al.
International Journal of Intelligent Networks (2022) Vol. 3, pp. 58-73
Open Access | Times Cited: 373
Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment
Hema Sekhar Reddy Rajula, Giuseppe Verlato, Mirko Manchia, et al.
Medicina (2020) Vol. 56, Iss. 9, pp. 455-455
Open Access | Times Cited: 372
Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells
Brent M. Kuenzi, Jisoo Park, Samson Fong, et al.
Cancer Cell (2020) Vol. 38, Iss. 5, pp. 672-684.e6
Open Access | Times Cited: 367
Why digital medicine depends on interoperability
Moritz Lehne, Julian Saß, Andrea Essenwanger, et al.
npj Digital Medicine (2019) Vol. 2, Iss. 1
Open Access | Times Cited: 350
The “inconvenient truth” about AI in healthcare
Trishan Panch, Heather Mattie, Leo Anthony Celi
npj Digital Medicine (2019) Vol. 2, Iss. 1
Open Access | Times Cited: 345
Edge Machine Learning for AI-Enabled IoT Devices: A Review
Massimo Merenda, Carlo Porcaro, Demetrio Iero
Sensors (2020) Vol. 20, Iss. 9, pp. 2533-2533
Open Access | Times Cited: 343
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
Prediction of the development of acute kidney injury following cardiac surgery by machine learning
Po-Yu Tseng, Yi‐Ting Chen, Chuen-Heng Wang, et al.
Critical Care (2020) Vol. 24, Iss. 1
Open Access | Times Cited: 325
Explainable artificial intelligence model to predict acute critical illness from electronic health records
Simon Meyer Lauritsen, Mads R. B. Kristensen, Mathias Vassard Olsen, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 319
Machine learning for precision medicine
Sarah J. MacEachern, Nils D. Forkert
Genome (2020) Vol. 64, Iss. 4, pp. 416-425
Closed Access | Times Cited: 314
Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic
Rajvikram Madurai Elavarasan, Rishi Pugazhendhi
The Science of The Total Environment (2020) Vol. 725, pp. 138858-138858
Open Access | Times Cited: 308
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
Yue Gao, Guangyao Cai, Wei Fang, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 306
Axes of a revolution: challenges and promises of big data in healthcare
Smadar Shilo, Hagai Rossman, Eran Segal
Nature Medicine (2020) Vol. 26, Iss. 1, pp. 29-38
Closed Access | Times Cited: 301
Artificial intelligence in oncology
Hideyuki Shimizu, Keiichi I. Nakayama
Cancer Science (2020) Vol. 111, Iss. 5, pp. 1452-1460
Open Access | Times Cited: 293
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
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