Topic · Machine Learning in Healthcare
Most cited papers in Machine Learning in Healthcare
What are the most cited papers in Machine Learning in Healthcare?
The most cited paper in Machine Learning in Healthcare is ""Why Should I Trust You?"" (Marco Túlio Ribeiro, Sameer Kumar Singh, Carlos Guestrin, 2016), with 16,496 citations. Next come "Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data" (10,878) and "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" (10,230).
Showing 50 of 99 papers. Every paper with more than 961 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | "Why Should I Trust You?" Marco Túlio Ribeiro, Sameer Kumar Singh, Carlos Guestrin | 8 Aug 2016 | 16,496 |
| 2 | Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data Hude Quan, Vijaya Sundararajan, Patricia Halfon et al. · Medical Care | 13 Oct 2005 | 10,878 |
| 3 | Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Cynthia D Rudin · Nature Machine Intelligence | 13 May 2019 | 10,230 |
| 4 | High-performance medicine: the convergence of human and artificial intelligence Eric J. Topol · Nature Medicine | 28 Dec 2018 | 10,125 |
| 5 | A Comprehensive Survey on Graph Neural Networks Zonghan Wu, Shirui Pan, Fengwen Chen et al. · IEEE Transactions on Neural Networks and Learning Systems | 24 Mar 2020 | 10,010 |
| 6 | A guide to deep learning in healthcare Andre Esteva, Alexandre Robicquet, Bharath Ramsundar et al. · Nature Medicine | 24 Dec 2018 | 5,244 |
| 7 | Artificial intelligence in healthcare: past, present and future Fei Jiang, Yong Jiang, Hui Zhi et al. · Stroke and Vascular Neurology | 21 Jun 2017 | 4,877 |
| 8 | Machine Learning in Medicine Alvin Rajkomar, Jay B. Dean, Isaac S. Kohane · New England Journal of Medicine | 3 Apr 2019 | 4,470 |
| 9 | Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement Professor Gary S. Collins, Johannes B. Reitsma, Douglas G. Altman et al. · BMJ | 7 Jan 2015 | 4,377 |
| 10 | Large language models in medicine Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan et al. · Nature Medicine | 17 Jul 2023 | 3,868 |
| 11 | Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla et al. · PLOS Digital Health | 9 Feb 2023 | 3,867 |
| 12 | The potential for artificial intelligence in healthcare Thomas H. Davenport, Ravi Kalakota · Future Healthcare Journal | Jun 2019 | 3,846 |
| 13 | Predicting the Future — Big Data, Machine Learning, and Clinical Medicine Ziad Obermeyer, Ezekiel Emanuel · New England Journal of Medicine | 28 Sep 2016 | 3,703 |
| 14 | Machine Learning in Medicine Rahul C. Deo · Circulation | 16 Nov 2015 | 3,562 |
| 15 | Deep learning for healthcare: review, opportunities and challenges Riccardo Miotto, Fei Wang, Shuang Wang et al. · Briefings in Bioinformatics | 5 Apr 2017 | 3,149 |
| 16 | Revolutionizing healthcare: the role of artificial intelligence in clinical practice Shuroug A. Alowais, Sahar S. Alghamdi, Nada Alsuhebany et al. · BMC Medical Education | 22 Sep 2023 | 3,123 |
| 17 | An overview of clinical decision support systems: benefits, risks, and strategies for success Reed Taylor Sutton, David Pincock, Daniel C. Baumgart et al. · npj Digital Medicine | 6 Feb 2020 | 3,082 |
| 18 | The future of digital health with federated learning Nicola Rieke, Jonny Hancox, Wenqi Li et al. · npj Digital Medicine | 14 Sep 2020 | 3,041 |
| 19 | Key challenges for delivering clinical impact with artificial intelligence Christopher J. Kelly, Alan Karthikesalingam, Mustafa Suleyman et al. · BMC Medicine | 29 Oct 2019 | 3,030 |
| 20 | Self-Attentive Sequential Recommendation Wang-Cheng Kang, Julian McAuley | Nov 2018 | 3,000 |
| 21 | Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success Kensaku Kawamoto, Caitlin A Houlihan, E. Andrew Balas et al. · BMJ | 14 Mar 2005 | 2,774 |
| 22 | CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison Jeremy Irvin, Pranav Rajpurkar, Michael Kwan-Lung Ko et al. · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 17 Jul 2019 | 2,717 |
| 23 | Scalable and accurate deep learning with electronic health records Alvin Rajkomar et al. · npj Digital Medicine | 4 May 2018 | 2,621 |
| 24 | BERT4Rec Fei Long Sun, Jun Liu, Jian Feng Wu et al. | 3 Nov 2019 | 2,506 |
| 25 | A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI Erico Tjoa, Cuntai Guan · IEEE Transactions on Neural Networks and Learning Systems | 21 Oct 2020 | 2,419 |
| 26 | Learning Important Features Through Propagating Activation Differences Avanti Shrikumar, Peyton Greenside, Anshul Kundaje · arXiv (Cornell University) | 10 Apr 2017 | 2,377 |
| 27 | Big Data and Machine Learning in Health Care Andrew L. Beam, Isaac S. Kohane · JAMA | 12 Mar 2018 | 2,347 |
| 28 | Artificial intelligence in medicine Pavel Hamet, Johanne Tremblay · Metabolism | 11 Jan 2017 | 2,326 |
| 29 | Recurrent Neural Networks for Multivariate Time Series with Missing Values Zhengping Che, Sanjay Purushotham, Kyunghyun Cho et al. · Scientific Reports | 11 Apr 2018 | 2,266 |
| 30 | Logistic regression and artificial neural network classification models: a methodology review Stephan Dreiseitl, Lucila Ohno‐Machado · Journal of Biomedical Informatics | Oct 2002 | 2,205 |
| 31 | A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models Evangelia F. CHRISTODOULOU, Jie Ma, Professor Gary S. Collins et al. · Journal of Clinical Epidemiology | 11 Feb 2019 | 2,132 |
| 32 | Explainable machine-learning predictions for the prevention of hypoxaemia during surgery Scott Lundberg, Bala G. Nair, Monica Shanta Vavilala et al. · Nature Biomedical Engineering | 28 Sep 2018 | 2,073 |
| 33 | DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network Jared Katzman, Uri Shaham, Alexander Cloninger et al. · BMC Medical Research Methodology | 26 Feb 2018 | 2,045 |
| 34 | Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence Vikas Hassija, Vinay Chamola, Atmesh Mahapatra et al. · Cognitive Computation | 24 Aug 2023 | 2,031 |
| 35 | Deep Learning for Health Informatics Daniele Ravì, Charence Wong, Fani Deligianni et al. · IEEE Journal of Biomedical and Health Informatics | 29 Dec 2016 | 2,025 |
| 36 | XAI—Explainable artificial intelligence David Gunning, Mark Stefik, Jaesik Choi et al. · Science Robotics | 18 Dec 2019 | 2,024 |
| 37 | Towards better clinical prediction models: seven steps for development and an ABCD for validation Ewout Willem Steyerberg, Yvonne Vergouwe · European Heart Journal | 4 Jun 2014 | 1,970 |
| 38 | Can AI Help in Screening Viral and COVID-19 Pneumonia? Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Abdullah Khandakar et al. · IEEE Access | 2020 | 1,953 |
| 39 | Foundation models for generalist medical artificial intelligence Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad et al. · Nature | 12 Apr 2023 | 1,928 |
| 40 | ChatGPT: five priorities for research Eva A.M. van Dis, Johan Bollen, Willem H. Zuidema et al. · Nature | 3 Feb 2023 | 1,886 |
| 41 | Causability and explainability of artificial intelligence in medicine Andreas Holzinger, Georg Langs, Helmut Denk et al. · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery | 2 Apr 2019 | 1,872 |
| 42 | Precision Medicine, AI, and the Future of Personalized Health Care Kevin B. Johnson, Wei‐Qi Wei, Dilhan Weeraratne et al. · Clinical and Translational Science | 22 Sep 2020 | 1,844 |
| 43 | Validation of a modified Early Warning Score in medical admissions Christian Peter Subbe · QJM | Oct 2001 | 1,805 |
| 44 | Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records Riccardo Miotto, Li Li, Brian A. Kidd et al. · Scientific Reports | 17 May 2016 | 1,802 |
| 45 | Mining electronic health records: towards better research applications and clinical care Peter Bjødstrup Jensen, Lars Juhl Jensen, Søren Brunak · Nature Reviews Genetics | 2 May 2012 | 1,720 |
| 46 | 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 | 18 Apr 2023 | 1,716 |
| 47 | Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis Benjamin Shickel, Patrick James Tighe, Azra Bihorac et al. · IEEE Journal of Biomedical and Health Informatics | 27 Oct 2017 | 1,709 |
| 48 | Text Data Augmentation for Deep Learning Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht · Journal Of Big Data | 19 Jul 2021 | 1,707 |
| 49 | The Inevitable Application of Big Data to Health Care Travis B. Murdoch, Allan S. Detsky · JAMA | 3 Apr 2013 | 1,707 |
| 50 | Intelligible Models for HealthCare Rich Caruana, Yin Lou, Johannes E. Gehrke et al. | 7 Aug 2015 | 1,700 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.