Playing to our human strengths to prepare medical students for the future
Open accessAbstract
We are living in an age where artificial intelligence and astounding technological advances are bringing truly remarkable change to healthcare. Medical knowledge and skills which form the core responsibility of doctors such as making diagnoses may increasingly be delivered by robots. Machines are gradually acquiring human abilities such as deep learning and empathy. What, then is the role of doctors in future healthcare? And what direction should medical schools be taking to prepare their graduates? This article will give an overview of the evolving technological landscape of healthcare and examine the issues undergraduate medical education may have to address. The experience at The University of Hong Kong will serve as a case study featuring several curricular innovations that aim to empower medical graduates with the capabilities to thrive in the future.
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Physicians roles are changing due to the superior efficacy of AI in dealing with many established physician practices, chemical synthesis planning, and drug discovery.
“Ultimately, physicians roles are changing due to the superior efficacy of AI in dealing with many established physician practices, 15 chemical synthesis planning, and drug discovery.”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [15]
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Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Frenk, J., Chen, L., Bhutta, Z. A., Cohen, J., Crisp, N., Evans, T., Fineberg, H., Garcia, P., Ke, Y., Kelley, P., et al. (2010) · The Lancet · doi:10.1016/s0140-6736(10)61854-5 · Cited by 5,960 · see citing works
A WHO report: framework for action on interprofessional education and collaborative practice.
GILBERT, J. H. V., Yan, J., & Hoffman, S. J. (2010) · PubMed · Cited by 1,116 · see citing works
Twelve tips for facilitating Millennials’ learning
Roberts, D. H., Newman, L. R., & Schwartzstein, R. M. (2012) · Medical Teacher · doi:10.3109/0142159x.2011.613498 · Cited by 251 · see citing works
Effect of an evidence-based website on healthcare usage: an interrupted time-series study
Spoelman, W. A., Bonten, T. N., de Waal, M. W. M., Drenthen, T., Smeele, I. J. M., Nielen, M. M. J., & Chavannes, N. H. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-013166 · Cited by 111 · see citing works
Phronesis or "practical wisdom" in medical education
Dowie, A. (2000) · Medical Teacher · doi:10.1080/01421590050006188 · Cited by 48 · see citing works
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Karaca, O., Çalışkan, S. A., & Demir, K. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02546-6 · Cited by 377 · see citing works
Grunhut, J., Wyatt, A. T., & Marques, O. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211036836 · Cited by 161 · see citing works
“Playing to our human strengths to prepare medical students for the future.(15) Chen J.”
BackgroundNeeds, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum
Grunhut, J., Marques, O., & Wyatt, A. T. M. (2022) · JMIR Medical Education · doi:10.2196/35587 · Cited by 166 · see citing works
Zarei, M., Eftekhari Mamaghani, H., Abbasi, A., & Hosseini, M.-S. (2024) · Medicina Clínica Práctica · doi:10.1016/j.mcpsp.2023.100422 · Cited by 70 · see citing works
Medical students need artificial intelligence and machine learning training
Pucchio, A., Eisenhauer, E. A., & Moraes, F. Y. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00846-2 · Cited by 61 · see citing works
Physicians’ required competencies in AI-assisted clinical settings: a systematic review
Schuitmaker, L., Drogt, J., Benders, M., & Jongsma, K. (2025) · British Medical Bulletin · doi:10.1093/bmb/ldae025 · Cited by 54 · see citing works
- Authors
- Chen, J.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Chen, Julie
- ORCID
- Chen, Julie
- Venue
- Korean Journal of Medical Education
- Publisher
- Korean Society of Medical Education
- Published
- 2017-09-01
- Volume
- 29
- Issue
- 3
- Pages
- 193-197
- ISSN
- 1225-8067 (ISSN-L)
- 2005-727X
- 2005-7288
- PMC
- PMC5583434
- OpenAlex
- W2751912040
- Cited by
- 22
- References
- 7
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 8 · 2019: 2
- FWCI
- 0.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Korean journal of medical education (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Health and Medical Education
- Keywords
- artificial intelligence in healthcare, medical education, undergraduate medical curriculum, future of medicine, human strengths, empathy, physician role, curriculum innovation, University of Hong Kong
- MeSH terms
- Artificial Intelligence, Clinical Competence, Curriculum, Delivery of Health Care, Education, Medical, Undergraduate, Empathy, Hong Kong, Humans, Learning, Physicians, Schools, Medical, Students, Medical, Teaching, Technology
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