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

Faye Yu Ci Ng; Arun James Thirunavukarasu; Haoran Cheng; Ting Fang Tan; Laura Gutierrez; Yanyan Lan; Jasmine Chiat Ling Ong; Yap Seng Chong; Kee Yuan Ngiam; Dean Ho; Tien Yin Wong; Kenneth Kwek; Finale Doshi-Velez; Catherine Lucey; Thomas Coffman; Daniel Shu Wei Ting

doi:10.1016/j.xcrm.2023.101230

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

Cell Rep Med. 2023 Oct 17;4(10):101230. doi: 10.1016/j.xcrm.2023.101230.

Authors

Faye Yu Ci Ng¹, Arun James Thirunavukarasu², Haoran Cheng³, Ting Fang Tan⁴, Laura Gutierrez⁴, Yanyan Lan⁵, Jasmine Chiat Ling Ong⁶, Yap Seng Chong⁷, Kee Yuan Ngiam⁸, Dean Ho⁹, Tien Yin Wong¹⁰, Kenneth Kwek¹¹, Finale Doshi-Velez¹², Catherine Lucey¹³, Thomas Coffman¹⁴, Daniel Shu Wei Ting¹⁵

Affiliations

¹ Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
² Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; University of Cambridge School of Clinical Medicine, Cambridge, UK; Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK.
³ Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Rollins School of Public Health, Emory University, Atlanta, GA, USA; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
⁴ Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore.
⁵ Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China.
⁶ Department of Pharmacy, Singapore General Hospital, Singapore, Singapore.
⁷ Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Dean's Office, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
⁸ Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Biomedical Engineering, School of Engineering, National University of Singapore, Singapore, Singapore.
⁹ Biomedical Engineering, School of Engineering, National University of Singapore, Singapore, Singapore; Insitute for Digital Medicine (WisDM), N.1 Institute for Health, National University of Singapore, Singapore, Singapore; Department of Pharmacology, National University of Singapore, Singapore, Singapore.
¹⁰ Tsinghua Medicine, Tsinghua University, Beijing, China.
¹¹ Chief Executive Office, Singapore General Hospital, SingHealth, Singapore, Singapore.
¹² Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
¹³ Executive Vice Chancellor and Provost Office, University of California, San Francisco, San Francisco, CA, USA.
¹⁴ Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
¹⁵ Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore; Byers Eye Institute, Stanford University, Palo Alto, CA, USA. Electronic address: daniel.ting@duke-nus.edu.sg.

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.

Keywords: artificial intelligence; curriculum; digital health; evidence-based medicine; medical education; medical school; medical training; teaching.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Curriculum
Education, Medical*
Evidence-Based Medicine / education
Humans