Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers

• Type of Material: Article
• Authors: Faye Yu Ci Ng, National University of Singapore; Arun James Thirunavukarasu, University of Cambridge; Haoran Cheng, Emory University; Ting Fang Tan, Singapore Health Service; Laura Gutierrez, Singapore Health Service; Yanyan Lan, Tsinghua University; Jasmine Chiat Ling Ong, Singapore General Hospital; Yap Seng Chong, National University of Singapore; Kee Yuan Ngiam, National University of Singapore; Dean Ho, National University of Singapore; Tien Yin Wong, Tsinghua University; Kenneth Kwek, Singapore General Hospital; Finale Doshi-Velez, Harvard University; Catherine Lucey, University of California, San Francisco; Thomas Coffman, National University of Singapore; Daniel Shu Wei Ting, National University of Singapore
• Language: English
• Date: 2023-10-17
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Author(s)
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): https://doi.org/10.1016%2Fj.xcrm.2023.101230
• Title of Journal or Parent Work: Cell Reports Medicine
• Volume: 4
• Issue: 10
• Start Page: 101230
• Grant/Funding Information: D.S.W.T. is supported by Duke-NUS Medical School (Duke-NUS/RSF/2021/0018 and 05/FY2020/EX/15-A58), the Agency for Science, Technology and Research (A20H4g2141 and H20C6a0032), and the National Medical Research Council, Singapore (NMRC/HSRG/0087/2018, MOH-000655-00, and MOH-001014-00).
• Keywords: medical school; artificial intelligence; medical teaching; curriculum; digital health; medical education; teaching; evidence-based medicine
• Research Categories: Health Sciences, Education

Abstract

Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as “consumers”, “translators”, or “developers”. The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM). We outline a core curriculum for AI education of future consumers, translators, and developers, emphasizing the links between AI and EBM, with suggestions for how teaching may be integrated into existing curricula. We consider the key barriers to implementation of AI in the medical curriculum: time, resources, variable interest, and knowledge retention. By improving AI literacy rates and fostering a translator- and developer-enriched workforce, innovation may be accelerated for the benefit of patients and practitioners.