Subfield · Health Informatics
Most cited papers in Health Informatics
What are the most cited papers in Health Informatics?
The most cited paper in Health Informatics is "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" (Cynthia D Rudin, 2019), with 10,230 citations. Next come "High-performance medicine: the convergence of human and artificial intelligence" (10,125) and "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI" (10,008).
Showing 50 of 99 papers. Every paper with more than 1,166 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | 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 |
| 2 | High-performance medicine: the convergence of human and artificial intelligence Eric J. Topol · Nature Medicine | 28 Dec 2018 | 10,125 |
| 3 | 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 | 26 Dec 2019 | 10,008 |
| 4 | BioBERT: a pre-trained biomedical language representation model for biomedical text mining Jinhyuk Lee, Wonjin Yoon, Sungdong Kim et al. · Bioinformatics | 5 Sep 2019 | 7,445 |
| 5 | Systematic review of research on artificial intelligence applications in higher education – where are the educators? Olaf Zawacki‐Richter, Victoria I. Marín, Melissa Bond et al. · International Journal of Educational Technology in Higher Education | 27 Oct 2019 | 6,707 |
| 6 | ChatGPT for good? On opportunities and challenges of large language models for education Enkelejda Kasneci et al. · Learning and Individual Differences | 9 Mar 2023 | 6,643 |
| 7 | Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) Amina Adadi, Mohammed Berrada · IEEE Access | 2018 | 6,253 |
| 8 | A guide to deep learning in healthcare Andre Esteva, Alexandre Robicquet, Bharath Ramsundar et al. · Nature Medicine | 24 Dec 2018 | 5,244 |
| 9 | 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 |
| 10 | Machine Learning in Medicine Alvin Rajkomar, Jay B. Dean, Isaac S. Kohane · New England Journal of Medicine | 3 Apr 2019 | 4,470 |
| 11 | Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy Yogesh Kumar Dwivedi et al. · International Journal of Information Management | 11 Mar 2023 | 4,365 |
| 12 | Large language models in medicine Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan et al. · Nature Medicine | 17 Jul 2023 | 3,868 |
| 13 | Large language models encode clinical knowledge Karan Singhal et al. · Nature | 12 Jul 2023 | 3,868 |
| 14 | 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 |
| 15 | The potential for artificial intelligence in healthcare Thomas H. Davenport, Ravi Kalakota · Future Healthcare Journal | Jun 2019 | 3,846 |
| 16 | Predicting the Future — Big Data, Machine Learning, and Clinical Medicine Ziad Obermeyer, Ezekiel Emanuel · New England Journal of Medicine | 28 Sep 2016 | 3,703 |
| 17 | TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods Professor Gary S. Collins et al. · BMJ | 16 Apr 2024 | 3,581 |
| 18 | The ethics of algorithms: Mapping the debate Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo et al. · Big Data & Society | Dec 2016 | 3,394 |
| 19 | 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 |
| 20 | The future of digital health with federated learning Nicola Rieke, Jonny Hancox, Wenqi Li et al. · npj Digital Medicine | 14 Sep 2020 | 3,041 |
| 21 | 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 |
| 22 | Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes Amit X. Garg, Neill K. J. Adhikari, Heather McDonald et al. · JAMA | 8 Mar 2005 | 2,988 |
| 23 | ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns Malik Sallam · Healthcare | 19 Mar 2023 | 2,928 |
| 24 | AI in health and medicine Pranav Rajpurkar, Emma Chen, Oishi Banerjee et al. · Nature Medicine | 2022 | 2,914 |
| 25 | Artificial intelligence in healthcare Kun‐Hsing Yu, Andrew L. Beam, Isaac S. Kohane · Nature Biomedical Engineering | 5 Oct 2018 | 2,799 |
| 26 | A Survey on Evaluation of Large Language Models Yupeng Chang, Xu Wang, Jindong Wang et al. · ACM Transactions on Intelligent Systems and Technology | 23 Jan 2024 | 2,791 |
| 27 | Scalable and accurate deep learning with electronic health records Alvin Rajkomar et al. · npj Digital Medicine | 4 May 2018 | 2,621 |
| 28 | Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum John W. Ayers, Adam Poliak, Mark H. Dredze et al. · JAMA Internal Medicine | 28 Apr 2023 | 2,604 |
| 29 | Chatting and cheating: Ensuring academic integrity in the era of ChatGPT Debby Cotton, Peter A. Cotton, J. Reuben Shipway · Innovations in Education and Teaching International | 13 Mar 2023 | 2,473 |
| 30 | ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope Partha Pratim Ray · Internet of Things and Cyber-Physical Systems | 2023 | 2,456 |
| 31 | 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 |
| 32 | Calibration: the Achilles heel of predictive analytics On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of , Ben Van Calster, David J. McLernon et al. · BMC Medicine | Dec 2019 | 2,384 |
| 33 | Privacy-Preserving Deep Learning Reza Shokri, Vitaly Shmatikov | 6 Oct 2015 | 2,329 |
| 34 | Artificial intelligence in medicine Pavel Hamet, Johanne Tremblay · Metabolism | 11 Jan 2017 | 2,326 |
| 35 | The practical implementation of artificial intelligence technologies in medicine Jianxing He, Sally L. Baxter, Jie Xu et al. · Nature Medicine | 24 Dec 2018 | 2,319 |
| 36 | Maximum Classifier Discrepancy for Unsupervised Domain Adaptation Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku et al. | Jun 2018 | 2,291 |
| 37 | Students’ voices on generative AI: perceptions, benefits, and challenges in higher education Cecilia Ka Yuk Chan, Wenjie Hu · International Journal of Educational Technology in Higher Education | 16 Jul 2023 | 2,230 |
| 38 | European Union Regulations on Algorithmic Decision Making and a “Right to Explanation” Bryce Goodman, Seth Flaxman · AI Magazine | Sep 2017 | 2,177 |
| 39 | How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment Aidan Gilson, Conrad W. Safranek, Thomas Yoyan Huang et al. · JMIR Medical Education | 8 Feb 2023 | 2,163 |
| 40 | Explainability for artificial intelligence in healthcare: a multidisciplinary perspective Julia Amann, Alessandro Blasimme, Effy Vayena et al. · BMC Medical Informatics and Decision Making | 30 Nov 2020 | 2,163 |
| 41 | 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 |
| 42 | 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 |
| 43 | 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 |
| 44 | What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature Chung Kwan Lo · Education Sciences | 18 Apr 2023 | 1,939 |
| 45 | A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis Xiaoxuan Liu, Livia Faes, Aditya U. Kale et al. · The Lancet Digital Health | 25 Sep 2019 | 1,936 |
| 46 | Foundation models for generalist medical artificial intelligence Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad et al. · Nature | 12 Apr 2023 | 1,928 |
| 47 | Conceptualizing AI literacy: An exploratory review Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu et al. · Computers and Education Artificial Intelligence | 2021 | 1,921 |
| 48 | ChatGPT: five priorities for research Eva A.M. van Dis, Johan Bollen, Willem H. Zuidema et al. · Nature | 3 Feb 2023 | 1,886 |
| 49 | Artificial intelligence in cancer imaging: Clinical challenges and applications Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath et al. · CA A Cancer Journal for Clinicians | 5 Feb 2019 | 1,886 |
| 50 | What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education Ahmed Tlili, Boulus Shehata, Michael Agyemang Adarkwah et al. · Smart Learning Environments | 22 Feb 2023 | 1,855 |
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.