Artificial intelligence in medical education: a cross-sectional needs assessment
Open accessAbstract
BACKGROUND: As the information age wanes, enabling the prevalence of the artificial intelligence age; expectations, responsibilities, and job definitions need to be redefined for those who provide services in healthcare. This study examined the perceptions of future physicians on the possible influences of artificial intelligence on medicine, and to determine the needs that might be helpful for curriculum restructuring. METHODS: A cross-sectional multi-centre study was conducted among medical students country-wide, where 3018 medical students participated. The instrument of the study was an online survey that was designed and distributed via a web-based service. RESULTS: Most of the medical students perceived artificial intelligence as an assistive technology that could facilitate physicians' access to information (85.8%) and patients to healthcare (76.7%), and reduce errors (70.5%). However, half of the participants were worried about the possible reduction in the services of physicians, which could lead to unemployment (44.9%). Furthermore, it was agreed that using artificial intelligence in medicine could devalue the medical profession (58.6%), damage trust (45.5%), and negatively affect patient-physician relationships (42.7%). Moreover, nearly half of the participants affirmed that they could protect their professional confidentiality when using artificial intelligence applications (44.7%); whereas, 16.1% argued that artificial intelligence in medicine might cause violations of professional confidentiality. Of all the participants, only 6.0% stated that they were competent enough to inform patients about the features and risks of artificial intelligence. They further expressed that their educational gaps regarding their need for "knowledge and skills related to artificial intelligence applications" (96.2%), "applications for reducing medical errors" (95.8%), and "training to prevent and solve ethical problems that might arise as a result of using artificial intelligence applications" (93.8%). CONCLUSIONS: The participants expressed a need for an update on the medical curriculum, according to necessities in transforming healthcare driven by artificial intelligence. The update should revolve around equipping future physicians with the knowledge and skills to effectively use artificial intelligence applications and ensure that professional values and rights are protected.
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15 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].”
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 [35]
The Methods (page unavailable) identify this as a study of medical students in Turkey, supporting its inclusion as one of the cited studies. Our copy does not classify Turkey as an LMIC or establish the citing paper’s count of 15 studies conducted in HICs. (LLM, conf=0.78)
Experimental — accuracy not yet measuredAI's perceived potential to enhance healthcare accessibility was a driver of enthusiasm, with 49% in Turkey.
“enhance healthcare accessibility (49% in Turkey) [35]”
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 [35]
The Results section, “Perceptions of the possible influences of AI on medicine” (page unavailable), says students favoured AI partly because they believed it could facilitate patients’ access to healthcare. It reports 76.7% for that belief, not the cited 49%. (LLM, conf=0.91)
Experimental — accuracy not yet measuredA survey of 3018 medical students revealed mixed feelings regarding the integration of LLMs.
“In the field of medical interviewing, a survey of 3018 medical students revealed mixed feelings regarding the integration of LLMs.”
From Enhancing Medical Interview Skills Through AI-Simulated Patient Interactions: Nonrandomized Controlled Trial (2024) · cites this paper as [14]
Methods > Data collection reports that 3018 medical students participated, although data from 2981 were analyzed. Discussion > Opinions on the value of AI describes favorable views alongside substantial concerns, supporting a characterization of mixed feelings about AI generally. The paper does not establish that the survey concerned LLM integration or medical interviewing. (LLM, conf=0.91)
Experimental — accuracy not yet measured93.8% of students in Turkey support structured AI education, particularly in the domains of knowledge, applications, and ethics.
“Surveys indicate strong backing for AI literacy training, with 81% of UK physicians4 and 63% of Canadian medical students recognizing its importance12, while an overwhelming 93.8% of students in Turkey support structured AI education, particularly in the domains of knowledge, applications, and ethics13.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [13]
Literature searches located 7 survey research studies focusing on student or faculty AI perspectives.
“Literature searches located 7 survey research studies focusing on student or faculty AI perspectives14–20; only three published AI training needs assessment studies were conducted in a U.S. medical school.15,19,21”
From A Training Needs Analysis for AI and Generative AI in Medical Education: Perspectives of Faculty and Students (2025) · cites this paper as [14]
With the rapid integration of AI into various aspects of health care delivery, its infiltration into medical education seems imminent.
“With the rapid integration of AI into various aspects of health care delivery, its infiltration into medical education seems imminent [4,5].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [5]
A cross-sectional country-wide survey of Turkish medical students (N = 3018) showed that 96.2% of participants reported an urgent need to integrate AI in theory and practical training courses to improve their clinical judgment and clinical decision-making abilities.
“A cross-sectional country-wide survey of Turkish medical students (N = 3018) showed that 96.2% of participants reported an urgent need to integrate AI in theory and practical training courses to improve their clinical judgment and clinical decision-making abilities [21].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [21]
The finding that many participants were concerned about the negative impact of AI on patient trust in healthcare was consistent with findings from other similar studies.
“which was consistent with findings from other similar studies.”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [21]
A Turkish study involving medical students (N = 3018) found students believed that using AI in medicine could devalue the medical profession (58.6%), damage patient trust (45.5%), negatively affect patient-physician relationships (42.7%), and violate professional confidentiality (44.7%).
“A Turkish study involving medical students (N = 3018) found students believed that using AI in medicine could devalue the medical profession (58.6%), damage patient trust (45.5%), negatively affect patient-physician relationships (42.7%), and violate professional confidentiality (44.7%) [21].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [21]
The finding that participants expressed positive attitudes toward integration of AI concepts in HPE curricula was consistent with previous studies.
“which was consistent with previous studies [21, 27, 30, 35, 36].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [21]
Studies from different parts of the world, such as Turkey, Australia, and India, demonstrated similar results and reported students had positive attitudes toward learning about AI technology during their education.
“Furthermore, studies from different parts of the world, such as Turkey [21], Australia [34], and India [17], demonstrated similar results and reported students had positive attitudes toward learning about AI technology during their education.”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [21]
Other publications acknowledge the importance of ethics in AI education but do not provide specific content or guidance.
“Although other publications acknowledge the importance of ethics in AI education, they do not provide specific content or guidance [36-39].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [38]
Commentaries or viewpoints were the predominant study design among the included studies.
“In addition to two reviews [4344] and one cross-sectional study among medical students [45], commentaries or ‘viewpoints’ were the predominant study design among the included studies [22234246474849].”
From Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices (2023) · cites this paper as [45]
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- Authors
- Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A.
- Affiliations
- Bursa Uludağ Üni̇versi̇tesi̇ (TR)
- Corresponding author
- Civaner, M. Murat
- Venue
- BMC Medical Education
- Published
- 2022-11-09
- Volume
- 22
- Issue
- 1
- Pages
- 772-772
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC9646274
- OpenAlex
- W4308730821
- Cited by
- 363
- References
- 28
- Citations by year
- 2026: 111 · 2025: 112 · 2024: 94 · 2023: 44
- FWCI
- 18.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Bursa Uludag University - AVESIS (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- artificial intelligence in healthcare, medical students' perceptions, needs assessment, medical curriculum reform, physician unemployment, trust in AI, physician-patient relationship, professional secrecy, AI ethics education, medical errors
- MeSH terms
- Artificial Intelligence, Cross-Sectional Studies, Curriculum, Education, Medical, Humans, Needs Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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