Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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: 2635

Showing 26-50 of 2635 citing articles:

Identifying Ethical Considerations for Machine Learning Healthcare Applications
Danton Char, Michael D. Abràmoff, Chris Feudtner
The American Journal of Bioethics (2020) Vol. 20, Iss. 11, pp. 7-17
Open Access | Times Cited: 283
Do as AI say: susceptibility in deployment of clinical decision-aids
Susanne Gaube, Harini Suresh, Martina Raue, et al.
npj Digital Medicine (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 281
Machine Learning and the Future of Cardiovascular Care
Giorgio Quer, Ramy Arnaout, Michael Henne, et al.
Journal of the American College of Cardiology (2021) Vol. 77, Iss. 3, pp. 300-313
Open Access | Times Cited: 265
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: 258
Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Medicinal Research Reviews (2020) Vol. 40, Iss. 4, pp. 1276-1314
Closed Access | Times Cited: 255
Artificial Intelligence for Drug Toxicity and Safety
Anna O. Basile, Alexandre Yahi, Nicholas P. Tatonetti
Trends in Pharmacological Sciences (2019) Vol. 40, Iss. 9, pp. 624-635
Open Access | Times Cited: 243
Gut microbiome, big data and machine learning to promote precision medicine for cancer
Giovanni Cammarota, Gianluca Ianiro, Anna M. Ahern, et al.
Nature Reviews Gastroenterology & Hepatology (2020) Vol. 17, Iss. 10, pp. 635-648
Closed Access | Times Cited: 242
Digital health for optimal supportive care in oncology: benefits, limits, and future perspectives
Matti Aapro, Paolo Bossi, Arvind Dasari, et al.
Supportive Care in Cancer (2020) Vol. 28, Iss. 10, pp. 4589-4612
Open Access | Times Cited: 231
Machine Learning for Cultural Heritage: A Survey
Marco Fiorucci, Marina Khoroshiltseva, Massimiliano Pontil, et al.
Pattern Recognition Letters (2020) Vol. 133, pp. 102-108
Open Access | Times Cited: 224
Machine learning for a sustainable energy future
Zhenpeng Yao, Yanwei Lum, Andrew Johnston, et al.
Nature Reviews Materials (2022) Vol. 8, Iss. 3, pp. 202-215
Open Access | Times Cited: 223
A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre
Carol Y. Cheung, Dejiang Xu, Ching‐Yu Cheng, et al.
Nature Biomedical Engineering (2020) Vol. 5, Iss. 6, pp. 498-508
Closed Access | Times Cited: 212
The need to separate the wheat from the chaff in medical informatics
Federico Cabitza, Andrea Campagner
International Journal of Medical Informatics (2021) Vol. 153, pp. 104510-104510
Open Access | Times Cited: 206
Fairness of artificial intelligence in healthcare: review and recommendations
Daiju Ueda, Taichi Kakinuma, Shohei Fujita, et al.
Japanese Journal of Radiology (2023) Vol. 42, Iss. 1, pp. 3-15
Open Access | Times Cited: 206
Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression
Nikolaos Koutsouleris, Dominic Dwyer, Franziska Degenhardt, et al.
JAMA Psychiatry (2020) Vol. 78, Iss. 2, pp. 195-195
Open Access | Times Cited: 198
Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review
Norah Alballa, Isra Al-Turaiki
Informatics in Medicine Unlocked (2021) Vol. 24, pp. 100564-100564
Open Access | Times Cited: 195
Artificial intelligence: disrupting what we know about services
Dora E. Bock, Jeremy S. Wolter, O. C. Ferrell
Journal of Services Marketing (2020) Vol. 34, Iss. 3, pp. 317-334
Closed Access | Times Cited: 190
Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence
Theodore Alexandrov
Annual Review of Biomedical Data Science (2020) Vol. 3, Iss. 1, pp. 61-87
Open Access | Times Cited: 189
Use of machine learning to analyse routinely collected intensive care unit data: a systematic review
Duncan Shillan, Jonathan A C Sterne, Alan R. Champneys, et al.
Critical Care (2019) Vol. 23, Iss. 1
Open Access | Times Cited: 188
Improved prediction of immune checkpoint blockade efficacy across multiple cancer types
Diego Chowell, Seong‐Keun Yoo, Cristina Valero, et al.
Nature Biotechnology (2021) Vol. 40, Iss. 4, pp. 499-506
Open Access | Times Cited: 184
Digital health technologies: opportunities and challenges in rheumatology
Daniel H. Solomon, Robert S. Rudin
Nature Reviews Rheumatology (2020) Vol. 16, Iss. 9, pp. 525-535
Closed Access | Times Cited: 180
Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP
Ke Wang, Jing Tian, Chu Zheng, et al.
Computers in Biology and Medicine (2021) Vol. 137, pp. 104813-104813
Open Access | Times Cited: 179
SHIFTing artificial intelligence to be responsible in healthcare: A systematic review
Haytham Siala, Yichuan Wang
Social Science & Medicine (2022) Vol. 296, pp. 114782-114782
Open Access | Times Cited: 179
Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification
Sebastian Rauschert, Kyle Raubenheimer, Phillip E. Melton, et al.
Clinical Epigenetics (2020) Vol. 12, Iss. 1
Open Access | Times Cited: 173
Evaluation of a decided sample size in machine learning applications
Daniyal Rajput, Wei-Jen Wang, Chun‐Chuan Chen
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 173
Application of Artificial Intelligence in Food Industry—a Guideline
Nidhi Rajesh Mavani, Jarinah Mohd Ali, Suhaili Othman, et al.
Food Engineering Reviews (2021) Vol. 14, Iss. 1, pp. 134-175
Open Access | Times Cited: 172

Scroll to top