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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:

Deep learning interpretation of echocardiograms
Amirata Ghorbani, David Ouyang, Abubakar Abid, et al.
npj Digital Medicine (2020) Vol. 3, Iss. 1
Open Access | Times Cited: 365

Showing 1-25 of 365 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: 1414
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: 851
Video-based AI for beat-to-beat assessment of cardiac function
David Ouyang, Bryan He, Amirata Ghorbani, et al.
Nature (2020) Vol. 580, Iss. 7802, pp. 252-256
Open Access | Times Cited: 666
Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)
Hui Wen Loh, Chui Ping Ooi, Silvia Seoni, et al.
Computer Methods and Programs in Biomedicine (2022) Vol. 226, pp. 107161-107161
Open Access | Times Cited: 426
A survey on deep learning in medicine: Why, how and when?
Francesco Piccialli, Vittorio Di Somma, Fabio Giampaolo, et al.
Information Fusion (2020) Vol. 66, pp. 111-137
Closed Access | Times Cited: 290
Transparency of deep neural networks for medical image analysis: A review of interpretability methods
Zohaib Salahuddin, Henry C. Woodruff, Avishek Chatterjee, et al.
Computers in Biology and Medicine (2021) Vol. 140, pp. 105111-105111
Open Access | Times Cited: 280
Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use
Akhil Narang, Richard Bae, Ha Hong, et al.
JAMA Cardiology (2021) Vol. 6, Iss. 6, pp. 624-624
Open Access | Times Cited: 269
Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints
Ohad Oren, Bernard J. Gersh, Deepak L. Bhatt
The Lancet Digital Health (2020) Vol. 2, Iss. 9, pp. e486-e488
Open Access | Times Cited: 239
Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology
Lior Drukker, J. Alison Noble, Aris T. Papageorghiou
Ultrasound in Obstetrics and Gynecology (2020) Vol. 56, Iss. 4, pp. 498-505
Open Access | Times Cited: 166
Applications of artificial intelligence in cardiovascular imaging
Maxime Sermesant, Hervé Delingette, Hubert Cochet, et al.
Nature Reviews Cardiology (2021) Vol. 18, Iss. 8, pp. 600-609
Closed Access | Times Cited: 120
Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks
Sajid Nazir, Diane M. Dickson, Muhammad Usman Akram
Computers in Biology and Medicine (2023) Vol. 156, pp. 106668-106668
Open Access | Times Cited: 120
High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning
Grant Duffy, Paul Cheng, Neal Yuan, et al.
JAMA Cardiology (2022) Vol. 7, Iss. 4, pp. 386-386
Open Access | Times Cited: 119
Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine
Sudipta Roy, Tanushree Meena, Se‐Jung Lim
Diagnostics (2022) Vol. 12, Iss. 10, pp. 2549-2549
Open Access | Times Cited: 108
Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging
Yunyan Zhang, Daphne Hong, Daniel G. McClement, et al.
Journal of Neuroscience Methods (2021) Vol. 353, pp. 109098-109098
Closed Access | Times Cited: 105
From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare
Chiranjib Chakraborty, Manojit Bhattacharya, Soumen Pal, et al.
Current Research in Biotechnology (2023) Vol. 7, pp. 100164-100164
Open Access | Times Cited: 61
The Role of Artificial Intelligence in Echocardiography
Timothy Barry, Juan Farina, Chieh‐Ju Chao, et al.
Journal of Imaging (2023) Vol. 9, Iss. 2, pp. 50-50
Open Access | Times Cited: 58
Severe aortic stenosis detection by deep learning applied to echocardiography
Gregory Holste, Evangelos K. Oikonomou, Bobak J. Mortazavi, et al.
European Heart Journal (2023) Vol. 44, Iss. 43, pp. 4592-4604
Open Access | Times Cited: 53
Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review
Celina Silvia Stafie, Irina-Georgeta Șufaru, Cristina Mihaela Ghiciuc, et al.
Diagnostics (2023) Vol. 13, Iss. 12, pp. 1995-1995
Open Access | Times Cited: 50
Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models
Ahmed Salih, Ilaria Boscolo Galazzo, Polyxeni Gkontra, et al.
Circulation Cardiovascular Imaging (2023) Vol. 16, Iss. 4
Open Access | Times Cited: 42
Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review
Maria Frasca, Davide La Torre, Gabriella Pravettoni, et al.
Discover Artificial Intelligence (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 38
Vision–language foundation model for echocardiogram interpretation
Matthew Christensen, Milos Vukadinovic, Neal Yuan, et al.
Nature Medicine (2024) Vol. 30, Iss. 5, pp. 1481-1488
Open Access | Times Cited: 31
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xi Xu, Jianqiang Li, Zhichao Zhu, et al.
Bioengineering (2024) Vol. 11, Iss. 3, pp. 219-219
Open Access | Times Cited: 30
Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization
Srikanth Ryali, Yuan Zhang, Carlo de los Angeles, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 9
Closed Access | Times Cited: 25
Comparing the Performance of Popular Large Language Models on the National Board of Medical Examiners Sample Questions
Ali Abbas, Mahad S Rehman, Syed Shakil Ur Rehman
Cureus (2024)
Open Access | Times Cited: 22
Using generative AI to investigate medical imagery models and datasets
Oran Lang, Doron Stupp, Ilana Traynis, et al.
EBioMedicine (2024) Vol. 102, pp. 105075-105075
Open Access | Times Cited: 18
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