Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland
Open accessAbstract
Leading figures in biomedical informatics advocate education in digital health for the healthcare workforce.[1 2][1] In healthcare, artificial intelligence/machine learning (AI/ML)-enabled tools increasingly play a role by informing patient triage decisions, clinical decision support systems, and
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Qualitative insights from Palestine and Ireland highlight students' preference for interactive, practice-oriented learning experiences, such as AI-assisted simulations, to enhance hands-on competence.
“Qualitative insights from Palestine [49] and Ireland [34] further highlight students’ preference for interactive, practice-oriented learning experiences, such as AI-assisted simulations, to enhance hands-on competence.”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [34]
The Discussion (page unavailable) reports that most surveyed Irish medical students thought AI/ML learning should be part of their medical degree. The Methods (page unavailable) describe a survey of exposure to and opinions about AI/ML education, not qualitative insights into preferred teaching methods. Our copy does not support a preference for interactive, practice-oriented learning or AI-assisted simulations to build hands-on competence; this verdict concerns only the Ireland citation. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThere is a disparity in awareness and understanding of AI and ChatGPT among healthcare students across different regions, and a lack of consensus concerning the role and utility of these technologies in medical education.
“These findings suggest a disparity in awareness and understanding of AI and ChatGPT among healthcare students across different regions, and reveal a lack of consensus concerning the role and utility of these technologies in medical education.”
From Health profession students' perceptions of ChatGPT in healthcare and education: insights from a mixed-methods study (2025) · cites this paper as [5]
The Results (page not provided) report limited AI/ML exposure and awareness among Irish medical students, and the Discussion says students were divided about whether to pursue further learning. The paper does not assess ChatGPT or establish differences in awareness across regions. Its finding that 78.6% favored including AI/ML in training also does not establish a lack of consensus about these technologies’ role and utility in medical education. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAlthough the number of respondents exceeded the required sample size, the response rate was lower compared with similar studies, which may limit the generalizability of the findings.
“First, although the number of respondents exceeded the required sample size, the response rate was lower compared with similar studies [19, 21, 22, 24, 25, 29, 31, 34, 37, 40, 62], which may limit the generalizability of the findings.”
From Knowledge and use, Perceptions of Benefits and Limitations of Artificial Intelligence Chatbots among Italian Physiotherapy students: a cross-sectional National Study (2025) · cites this paper as [19]
The Discussion (page unavailable) says the 43% response rate raises questions about representativeness, supporting the concern about generalizability. The Results (page unavailable) report 252 respondents, but the paper does not say that this exceeded a required sample size or that its response rate was lower than those of similar studies. (LLM, conf=0.85)
Experimental — accuracy not yet measured15 included studies were conducted in high-income countries and the remaining studies in low- and middle-income countries.
“Specifically, 15 studies were conducted in HICs [26–40], while other studies were conducted in LMICs [23, 24, 41–49].”
Male students and those with STEM backgrounds reported higher confidence in AI applications.
“male students and those with STEM (science, technology, engineering, and mathematics) backgrounds reported higher confidence in AI applications”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [34]
The Results (page not provided) report that male respondents were more likely to have heard of machine learning and to plan to learn about AI/ML. They do not report greater confidence in AI applications. Searches for confidence and STEM backgrounds found no comparison supporting the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Female and non-STEM students expressed greater ethical concerns about AI.
“whereas female and non-STEM students expressed greater ethical concerns [34, 51]”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [34]
The Results report gender differences in awareness of machine learning and willingness to learn about AI/ML, not ethical concerns. The Introduction and Discussion mention ethical issues but do not compare female or non-STEM students with other students on those concerns. Searches for ethical concerns by gender and STEM background found no supporting passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe mean respondent years of experience working with AI was only reported in three studies.
“The mean respondent years of experience working with AI was only reported in three studies [7,21,28].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [28]
Medical students are generally enthusiastic about learning and using AI in their practice.
“However, they are generally enthusiastic about learning and using AI in their practice [16, 17].”
From AI in medical education: medical student perception, curriculum recommendations and design suggestions (2023) · cites this paper as [17]
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Cites
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Cited by
Sorted by citations
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · see citing works
Artificial intelligence in medical education: a cross-sectional needs assessment
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The cases for and against artificial intelligence in the medical school curriculum
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“However, studies have primarily focused on some disciplines such as medicine [19–21, 23, 25, 26, 29, 30, 33, 34, 36, 37, 41–44], a mixture of health professions [22, 28, 36, 38, 39, 42], pharmacy [24, 32, 40, 42], dentistry [27, 36], and veterinary medicine [31], leaving other healthcare fields…”
InfluentialBlease, C., Kharko, A., Bernstein, M., Bradley, C., Houston, M., Walsh, I., & D Mandl, K. (2023) · JMIR Medical Education · doi:10.2196/42639 · Cited by 10 · see citing works
Self-citationMicallef Grimaud, L., & Gathu, C. (2024) · doi:10.2196/preprints.57746 · Cited by 3 · see citing works
Mastour, H., Mirzaei, S., Sohrabi, S., & Toofaninejad, E. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09630-9 · Cited by 3 · see citing works
“Ethical and social concerns include data privacy, reduced human interaction, AI reliability, and trust issues arising from algorithmic biases [28, 34, 41, 45, 48].”
Influential
- Authors
- Blease, C., Kharko, A., Bernstein, M., Bradley, C., Houston, M., Walsh, I., Hägglund, M., DesRoches, C., & Mandl, K. D.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Uppsala University (SE)
- Brown University (US)
- University College Cork (IE)
- Ollscoil na Gaillimhe – University of Galway (IE)
- Queen's University Belfast (GB)
- Boston Children's Hospital (US)
- Corresponding author
- Blease, Charlotte
- ORCID
- Venue
- BMJ Health & Care Informatics
- Publisher
- BMJ
- Published
- 2022-02
- Volume
- 29
- Issue
- 1
- Pages
- e100480
- ISSN
- 2632-1009 (ISSN-L)
- PMC
- PMC8808371
- OpenAlex
- W4210404118
- Cited by
- 81
- References
- 4
- Citations by year
- 2026: 13 · 2025: 21 · 2024: 23 · 2023: 16 · 2022: 8
- FWCI
- 5.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Health & Care Informatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research Portal (Queen's University Belfast) (publishedVersion, cc-by-nc)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lenus, The Irish Health Repository (Dr Steevens Hospital Library) (submittedVersion, cc-by-nc)
- Funders
- Uppsala University (FORTE—the Swedish Research Council for Health, Working Life and Welfare through the research project “Beyond Implementation of eHealth” (2020-01229))
- 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
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- machine learning, artificial intelligence in healthcare, medical education, medical students, student perceptions, digital health education, clinical decision support, triage, Ireland
- MeSH terms
- Machine Learning, Education, Medical, Humans, Ireland, Surveys and Questionnaires, Students, Medical
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