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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:

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

Showing 1-25 of 1610 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
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
Explainable AI: A Review of Machine Learning Interpretability Methods
Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis
Entropy (2020) Vol. 23, Iss. 1, pp. 18-18
Open Access | Times Cited: 1900
Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher, Bastian Bohn, Marco F. Duarte, et al.
IEEE Access (2020) Vol. 8, pp. 42200-42216
Open Access | Times Cited: 754
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
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 5, pp. 820-838
Open Access | Times Cited: 698
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
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
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: 427
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Communications in computer and information science (2020), pp. 417-431
Open Access | Times Cited: 411
Interpretable Machine Learning
Valerie Chen, Jeffrey Li, Joon Sik Kim, et al.
Queue (2021) Vol. 19, Iss. 6, pp. 28-56
Open Access | Times Cited: 396
Interpretability of machine learning‐based prediction models in healthcare
Gregor Štiglic, Primož Kocbek, Nino Fijačko, et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2020) Vol. 10, Iss. 5
Open Access | Times Cited: 379
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: 369
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Waddah Saeed, Christian W. Omlin
Knowledge-Based Systems (2023) Vol. 263, pp. 110273-110273
Open Access | Times Cited: 357
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
Alon Jacovi, Yoav Goldberg
(2020)
Open Access | Times Cited: 323
Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models
Xilei Zhao, Xiang Yan, Alan S.L. Yu, et al.
Travel Behaviour and Society (2020) Vol. 20, pp. 22-35
Closed Access | Times Cited: 283
Explainable Machine Learning in Credit Risk Management
Niklas Bussmann, Paolo Giudici, Dimitri Marinelli, et al.
Computational Economics (2020) Vol. 57, Iss. 1, pp. 203-216
Open Access | Times Cited: 280
Biologically informed deep neural network for prostate cancer discovery
Haitham Elmarakeby, Justin H. Hwang, Rand Arafeh, et al.
Nature (2021) Vol. 598, Iss. 7880, pp. 348-352
Open Access | Times Cited: 272
Artificial intelligence and machine learning for medical imaging: A technology review
Ana María Barragán Montero, Umair Javaid, Gilmer Valdés, et al.
Physica Medica (2021) Vol. 83, pp. 242-256
Open Access | Times Cited: 270
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Khansa Rasheed, Adnan Qayyum, Junaid Qadir, et al.
IEEE Reviews in Biomedical Engineering (2020) Vol. 14, pp. 139-155
Open Access | Times Cited: 258
Interpretable machine learning for knowledge generation in heterogeneous catalysis
Jacques A. Esterhuizen, Bryan R. Goldsmith, Suljo Linic
Nature Catalysis (2022) Vol. 5, Iss. 3, pp. 175-184
Closed Access | Times Cited: 254
Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond
Xuhong Li, Haoyi Xiong, Xingjian Li, et al.
Knowledge and Information Systems (2022) Vol. 64, Iss. 12, pp. 3197-3234
Open Access | Times Cited: 252
Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China
Jichong Han, Zhao Zhang, Juan Cao, et al.
Remote Sensing (2020) Vol. 12, Iss. 2, pp. 236-236
Open Access | Times Cited: 241
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Meike Nauta, Jan Trienes, Shreyasi Pathak, et al.
ACM Computing Surveys (2023) Vol. 55, Iss. 13s, pp. 1-42
Open Access | Times Cited: 235
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