Machine learning and medical education
Open accessAbstract
Artificial intelligence (AI) driven by machine learning (ML) algorithms is a branch in computer science that is rapidly gaining popularity within the healthcare sector. Recent regulatory approvals of AI-driven companion diagnostics and other products are glimmers of a future in which these tools could play a key role by defining the way medicine will be practiced. Educating the next generation of medical professionals with the right ML techniques will enable them to become part of this emerging data science revolution.
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The current medical school curriculum is unable to accommodate AI due to insufficient time and lack of expertise.
“This has been recently discussed by Kolachalama and Garg [47]; the current medical school curriculum is unable to accommodate AI due to two main reasons—insufficient time and lack of expertise.”
From Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review (2019) · cites this paper as [47]
The reviewed literature suggests a consensus on the importance of AI education in the medical curriculum but a void in actual planning and implementation of such education.
“Our results show that the literature suggests a consensus of opinions regarding the importance of AI education in medical curriculum but a void in actual planning and implementation of this.43,46,48”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [43]
AI can improve medical practice, address staff shortages, and enhance diagnostic efficiency.
“Artificial intelligence (AI) is transforming multiple industries, including healthcare and medical education, by enhancing diagnostic accuracy, supporting clinical decision-making, and addressing workforce shortages [1, 2].”
From Knowledge, attitudes, and practices toward AI technology (ChatGPT) among nursing students at Palestinian universities (2025) · cites this paper as [1]
Research recommends teaching AI ethics early in medical education to prepare for its potential impacts and challenges.
“Research recommends teaching AI ethics early in medical education to prepare for its potential impacts and challenges [6-8].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [8]
Educating the next generation of physicians with the right techniques and adaptations to AI will enable them to become part of the emerging data science revolution.
“Educating the next generation of physicians with the right techniques and adaptations to AI will enable them to become part of this emerging data science revolution [8].”
From Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum (2022) · cites this paper as [8]
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References 14 · Cited by 320
Research works printed but not linked: 1
- [15]Wald HS George P Reis SP Taylor JS Electronic health record training in undergraduate medical education: bridging theory to practice with curricula for empowering patient- and relationship-centered care in the computerized setting Acad. Med. 2014 89 380 386 10.1097/ACM.0000000000000131 24448045 · DOI
- Authors
- Kolachalama, V. B., & Garg, P. S.
- Affiliations
- Boston University (US)
- Corresponding author
- Kolachalama, Vijaya B.
- Venue
- npj Digital Medicine
- Published
- 2018-09-27
- Volume
- 1
- Issue
- 1
- Pages
- 54-54
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6550167
- OpenAlex
- W2889976627
- Cited by
- 320
- References
- 14
- Citations by year
- 2026: 40 · 2025: 46 · 2024: 65 · 2023: 38 · 2022: 45 · 2021: 41 · 2020: 34 · 2019: 11
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- American Heart Association (17SDG33670323)
- School of Medicine, Boston University (1UL1TR001430)
- National Institutes of Health (1r56ag062109-01, UL1TR001430)
- Clinical and Translational Science Institute, Boston University (1UL1TR001430)
- National Institute on Aging (R56-AG062109, RF1AG062109)
- U.S. National Library of Medicine
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, medical education, artificial intelligence in healthcare, companion diagnostics, AI regulation, physician training
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