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Cited by 108Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties

Open access
Busch, Felix, Hoffmann, Lena, Truhn, Daniel, Ortiz-Prado, Esteban, Makowski, Marcus R., Bressem, Keno K., Adams, Lisa C., COMFORT Consortium +108 more·2024·BMC Medical Education
DOI 10.1186/s12909-024-06035-4PMC PMC11439199

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Abstract

Background: The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions? Methods: An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-field comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann–Whitney U-test, Kruskal–Wallis test, and Dunn-Bonferroni post hoc test. Results: The survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3–4) and a desire for more AI teaching (median: 4, IQR: 4–5). However, they had limited AI knowledge (median: 2, IQR: 2–2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1–3). Subgroup analyses revealed significant differences between the Global North and South ( r = 0.025 to 0.185, all P < .001) and across continents ( r = 0.301 to 0.531, all P < .001), with generally small effect sizes. Conclusions: This large-scale international survey highlights medical, dental, and veterinary students' positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs. Trial registration: Not applicable (no clinical trial).

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  • Global South students report substantially less AI integration in curricula and lower preparedness levels.

    “Global South students report substantially less AI integration in curricula and lower preparedness levels (Busch et al., 2024; Rincón et al., 2025).”

    From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Busch et al., 2024

    The Discussion says Global South students were generally less likely than Global North students to report AI in their curricula. But the Results (Regional comparisons) report the opposite of the preparedness claim: Global South students felt more prepared to use AI in future practice. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 3

  1. [37]Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019;5(1):e13930. 31199295 PMC6598417 · DOI · Paper page
  2. [48]Schinkel K, Nannan M, Panday R, Car J. Introducing Artificial Intelligence Training in Medical Education. JMIR Med Educ. 2019;5(2):e16048. 10.2196/16048. 31793895 PMC6918207 · DOI · Paper page
  3. [55]Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4768–77.

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Authors
Busch, F., Hoffmann, L., Truhn, D., Ortiz-Prado, E., Makowski, M. R., Bressem, K. K., Adams, L. C., COMFORT Consortium, Abdala, N., Navarro, Á. A., Aerts, H. J. W. L., Águas, C., Aineseder, M., Alomar, M., Angkurawaranon, S., Angus, Z. G., Asouchidou, E., Bakhshi, S., Bamidis, P. D., Barbosa, P. N. V. P., Bayramov, N. Y., Billis, A., Bitencourt, A. G. V., Becerra, A. J., Busomoke, F., Capela, A., Cau, R., Clements, W., Corlateanu, A., Cuocolo, R., Cương, N. N., Gama, Z., de Medeiros, P. J., de Velasco, G., Desai, V. B., Dhakal, A. K., Dignum, V., Domitrz, I., Ferrarotti, C., Fułek, K., Ganguly, S., García-Juárez, I., Savevska, C. G., Radovikj, M. G., Gorelik, N., Gorelik, V., Gorospe, L., Griffin, I., Grzybowski, A., Hering, A., Hide, M., Hochhegger, B., Hofstaetter, J. G., Huseynova, M. R., Iaconi, O.-S., Torres, P. I., Ivanova, N. G., Izquierdo-Condoy, J. S., Jackson, A. B., Jha, A. K., Jha, N., Jiang, L., Kader, R., Kaul, P., Kaya, G., Kępczyńska, K., Kolawole, I. K., Kolostoumpis, G., Koshy, A., Kruger, N. A., Loeser, A., Lucijanic, M., Maihoub, S., McFadden, S., Avila, M. C., Mihalčin, M., Miyake, M., Mogami, R., Molnár, A., Morakote, W., Ngabonziza, I., Ngo, T. Q., Nguyen, T. T., Nortje, M., Palaian, S., de Almeida, R. P., Perić, B., Pilko, G., Bates, M. L., Purwoko, M., Rainey, C., Ribeiro, J. C., Rodriguez-Granillo, G. A., Agudelo, N. R., Saba, L., Sadasivan, S., Sado, K., Saidman, J. M., Saturno-Hernandez, P. J., Schwarz, G. M., Solis-Barquero, S. M., Pérez-Olivares, J. S., Sountoulides, P., Stanzione, A., Tabakova, N. G., Takeda, K., Tanioka, S., Thulesius, H. O., Toapanta-Yanchapaxi, L. N., Truong, M. H., Tuncel, M., van Dijk, E. H. C., van Wijngaarden, P., Xu, L., Zatoński, T., & Zhang, L.
Affiliations
  • TUM Klinikum (DE)
  • Humboldt-Universität zu Berlin (DE)
  • Universitätsklinikum Aachen (DE)
  • Deutsches Herzzentrum München (DE)
  • Ss. Cyril and Methodius University in Skopje (MK)
  • Hue University (VN)
Corresponding author
Busch, Felix
ORCID
Venue
BMC Medical Education
Publisher
Springer Science and Business Media LLC
Published
2024-09-28
Volume
24
Issue
1
Pages
1066-1066
ISSN
1472-6920 (ISSN-L)
DOI
10.1186/s12909-024-06035-4
PMC
PMC11439199
OpenAlex
W4402945053
Cited by
108
References
53
Citations by year
2026: 65 · 2025: 38 · 2024: 5
FWCI
10.10
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Education (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, cc-by)
  • Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by)
  • Cork Open Research Archive (University College Cork, Ireland) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • mediaTUM (Technical University of Munich) (submittedVersion)
  • RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
  • RWTH Publications (RWTH Aachen)
Funders
  • HORIZON EUROPE Framework Programme (101079894)
  • Charité - Universitätsmedizin Berlin
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, dental students, veterinary students, student attitudes toward AI, artificial intelligence in education, AI knowledge, technological literacy, regional variation, Global North and Global South, medical curriculum, dental curriculum, veterinary curriculum, clinical implementation of AI
MeSH terms
Adult, Artificial Intelligence, Attitude of Health Personnel, Cross-Sectional Studies, Curriculum, Education, Dental, Education, Medical, Education, Veterinary, Female, Humans, Male, Surveys and Questionnaires, Students, Dental, Students, Medical, Young Adult

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