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

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.

PMID: 37852174
PMCID: PMC10591047
DOI: 10.1016/j.xcrm.2023.101230

Review

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

Faye Yu Ci Ng et al. Cell Rep Med. 2023.

. 2023 Oct 17;4(10):101230.

doi: 10.1016/j.xcrm.2023.101230.

Authors

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.

PMID: 37852174
PMCID: PMC10591047
DOI: 10.1016/j.xcrm.2023.101230

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.

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Conflict of interest statement

Declaration of interests D.H. is a co-inventor listed in current and pending patents pertaining to AI-based personalized medicine. D.S.W.T. and T.Y.W. are the co-inventors of a deep learning system for retinal diseases.

Figures

**Figure 1**
Schematic capturing the distinction between consumers, translators, and developers in medical AI While all have and require knowledge and skills relating to technical concepts, ethics, validation, and appraisal, the depth of understanding and ability is greater for developers than translators and greater for translators than consumers.

**Figure 2**
A proposed AI curriculum for healthcare courses, with concepts organized in four pillars along a consumer-translator-developer continuum Many concepts link closely with EBM, and foundational clinical and scientific knowledge remains essential.

See this image and copyright information in PMC

References

1. Secinaro S., Calandra D., Secinaro A., Muthurangu V., Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med. Inform. Decis. Mak. 2021;21:125. - PMC - PubMed
1. Blasiak A., Truong A.T.L., Remus A., Hooi L., Seah S.G.K., Wang P., Chye D.H., Lim A.P.C., Ng K.T., Teo S.T., et al. The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens. NPJ Digit. Med. 2022;5 83–12. - PMC - PubMed
1. Ho D. Artificial intelligence in cancer therapy. Science. 2020;367:982–983. - PubMed
1. Tan T.F., Thirunavukarasu A.J., Jin L., Lim J., Poh S., Teo Z.L., Ang M., Chan R.V.P., Ong J., Turner A., et al. Artificial intelligence and digital health in global eye health: opportunities and challenges. Lancet. Glob. Health. 2023;11:e1432–e1443. - PubMed
1. Matheny M., Israni S.T., Ahmed M., Whicher D., editors. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. National Academy of Medicine; 2019. - PubMed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources