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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:

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin
Nature Machine Intelligence (2019) Vol. 1, Iss. 5, pp. 206-215
Open Access | Times Cited: 5766

Showing 1-25 of 5766 citing articles:

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, et al.
Information Fusion (2019) Vol. 58, pp. 82-115
Open Access | Times Cited: 5967
Machine learning and deep learning
Christian Janiesch, Patrick Zschech, Kai Heinrich
Electronic Markets (2021) Vol. 31, Iss. 3, pp. 685-695
Open Access | Times Cited: 1762
Definitions, methods, and applications in interpretable machine learning
William J. Murdoch, Chandan Singh, Karl Kumbier, et al.
Proceedings of the National Academy of Sciences (2019) Vol. 116, Iss. 44, pp. 22071-22080
Open Access | Times Cited: 1610
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1553
ChatGPT: five priorities for research
Eva A.M. van Dis, Johan Bollen, Willem Zuidema, et al.
Nature (2023) Vol. 614, Iss. 7947, pp. 224-226
Closed Access | Times Cited: 1329
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Kaustav Bera, Kurt A. Schalper, David L. Rimm, et al.
Nature Reviews Clinical Oncology (2019) Vol. 16, Iss. 11, pp. 703-715
Open Access | Times Cited: 1156
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
Julia Amann, Alessandro Blasimme, Effy Vayena, et al.
BMC Medical Informatics and Decision Making (2020) Vol. 20, Iss. 1
Open Access | Times Cited: 1069
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
Aaron Fisher, Cynthia Rudin, Francesca Dominici
arXiv (Cornell University) (2018)
Open Access | Times Cited: 941
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: 740
Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 735
Drug discovery with explainable artificial intelligence
José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider
Nature Machine Intelligence (2020) Vol. 2, Iss. 10, pp. 573-584
Open Access | Times Cited: 720
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs, et al.
Medical Image Analysis (2022) Vol. 79, pp. 102470-102470
Open Access | Times Cited: 656
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Sajid Ali, Tamer Abuhmed, Shaker El–Sappagh, et al.
Information Fusion (2023) Vol. 99, pp. 101805-101805
Open Access | Times Cited: 640
Frontiers in Artificial Intelligence and Applications
Jaya Yadav, Shruti Shukla, Kanika Sharma, et al.
(2022), pp. 1-6
Closed Access | Times Cited: 639
Deep learning in cancer diagnosis, prognosis and treatment selection
Khoa Tran, Olga Kondrashova, Andrew P. Bradley, et al.
Genome Medicine (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 588
Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration
Fiona Fui‐Hoon Nah, Ruilin Zheng, Jingyuan Cai, et al.
Journal of Information Technology Case and Application Research (2023) Vol. 25, Iss. 3, pp. 277-304
Open Access | Times Cited: 577
Interpretable machine learning: Fundamental principles and 10 grand challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen, et al.
Statistics Surveys (2022) Vol. 16, Iss. none
Open Access | Times Cited: 565
Deep neural network models for computational histopathology: A survey
Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
Medical Image Analysis (2020) Vol. 67, pp. 101813-101813
Open Access | Times Cited: 562
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
Konstantinos C. Siontis, Peter A. Noseworthy, Zachi I. Attia, et al.
Nature Reviews Cardiology (2021) Vol. 18, Iss. 7, pp. 465-478
Open Access | Times Cited: 560
Fooling LIME and SHAP
Dylan Slack, Sophie Hilgard, Emily Jia, et al.
(2020), pp. 180-186
Open Access | Times Cited: 532
Explainable artificial intelligence: an analytical review
Plamen Angelov, Eduardo Soares, Richard Jiang, et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2021) Vol. 11, Iss. 5
Open Access | Times Cited: 511
Attention in Natural Language Processing
Andrea Galassi, Marco Lippi, Paolo Torroni
IEEE Transactions on Neural Networks and Learning Systems (2020) Vol. 32, Iss. 10, pp. 4291-4308
Open Access | Times Cited: 510
Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, et al.
(2020), pp. 648-657
Open Access | Times Cited: 501
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: 491
The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies
Aniek F. Markus, Jan A. Kors, Peter R. Rijnbeek
Journal of Biomedical Informatics (2020) Vol. 113, pp. 103655-103655
Open Access | Times Cited: 486
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