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

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

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, Abdala, Nitamar, Navarro, Álvaro Aceña, Aerts, Hugo J. W. L, Águas, Catarina, Aineseder, Martina, Alomar, Muaed, Angkurawaranon, Salita, Angus, Zachary G., Asouchidou, Eirini, Bakhshi, Sameer, Bamidis, Panagiotis D., Barbosa, Paula N. V. P., Bayramov, Nuru Y., Billis, Antonios, Bitencourt, Almir G. V., Becerra, Antonio JBollas, Busomoke, Fabrice, Capela, Andreia, Cau, Riccardo, Clements, Warren, Corlateanu, Alexandru, Cuocolo, Renato, Cương, Nguyễn N., Gama, Zenewton, de Medeiros, Paulo J., de Velasco, Guillermo, Desai, Vijay B., Dhakal, Ajaya K., Dignum, Virginia, Domitrz, Izabela, Ferrarotti, Carlos, Fułek, Katarzyna, Ganguly, Shuvadeep, García-Juárez, Ignacio, Savevska, Cvetanka Gjerakaroska, Radovikj, Marija Gjerakaroska, Gorelik, Natalia, Gorelik, Valérie, Gorospe, Luis, Griffin, Ian, Grzybowski, Andrzej, Hering, Alessa, Hide, Michihiro, Hochhegger, Bruno, Hofstaetter, Jochen G., Huseynova, Mehriban R., Iaconi, Oana-Simina, Torres, Pedro Iturralde, Ivanova, Nevena G., Izquierdo-Condoy, Juan S., Jackson, Aidan B., Jha, Ashish K., Jha, Nisha, Jiang, Lili, Kader, Rawen, Kaul, Padma, Kaya, Gürsan, Kępczyńska, Katarzyna, Kolawole, Israel K., Kolostoumpis, George, Koshy, Abraham, Kruger, Nicholas A., Loeser, Alexander, Lucijanic, Marko, Maihoub, Stefani, McFadden, Sonyia, Avila, Maria CMendez, Mihalčin, Matúš, Miyake, Masahiro, Mogami, Roberto, Molnár, András, Morakote, Wipawee, Ngabonziza, Issa, Ngo, Trung Q., Nguyen, Thanh T., Nortje, Marc, Palaian, Subish, de Almeida, Rui PPereira, Perić, Barbara, Pilko, Gašper, Bates, Monserrat LPuntunet, Purwoko, Mitayani, Rainey, Clare, Ribeiro, João C., Rodriguez-Granillo, Gaston A., Agudelo, Nicolás Rozo, Saba, Luca, Sadasivan, Shine, Sado, Keina, Saidman, Julia M., Saturno-Hernandez, Pedro J., Schwarz, Gilbert M., Solis-Barquero, Sergio M., Pérez-Olivares, Javier Soto, Sountoulides, Petros, Stanzione, Arnaldo, Tabakova, Nikoleta G., Takeda, Konagi, Tanioka, Satoru, Thulesius, Hans O., Toapanta-Yanchapaxi, Liz N., Truong, Minh H., Tuncel, Murat, van Dijk, Elon H. C., van Wijngaarden, Peter, Xu, Lina, Zatoński, Tomasz, Zhang, Longjiang·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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Fig. 1 The world map displays the geographical distribution of participating institutions (blue dots) in relation to the number of respondents per institution
Fig. 1 The world map displays the geographical distribution of participating institutions (blue dots) in relation to the number of respondents per institutionCC BY · PubMed Central
Fig. 2 Diverse perspectives from medical students on the integration of artificial intelligence (AI) in healthcare education and practice. The selected quotes reflect a range of sentiments, from concerns about dehumanization and potential challenges in low-resource settings to viewing AI as a beneficial tool that complements rather than replaces the human touch in medicine
Fig. 2 Diverse perspectives from medical students on the integration of artificial intelligence (AI) in healthcare education and practice. The selected quotes reflect a range of sentiments, from concerns about dehumanization and potential challenges in low-resource settings to viewing AI as a beneficial tool that complements rather than replaces the human touch in medicineCC BY · PubMed Central
Fig. 3 Pie charts illustrating student responses at the country level for the item "As part of my studies, there are curricular events on artificial intelligence (AI) in medicine.". A more filled, darker red chart indicates a higher proportion of students reporting no AI events, while a less filled, greener chart indicates fewer students reporting the absence of AI events. The missing portion of each chart displays the proportion of students who reported AI events, regardless of the duration. An all-white pie chart indicates that all students reported AI events in the medical curriculum. The absolute number of responses per country is shown above each chart. Analysis of the pie charts from countries with a representative sample of at least 50 respondents reveals that, among 28 nations, only four (Indonesia, Switzerland, Vietnam, and China) exhibited over 50% of students reporting the inclusion of AI events within their medical curriculum. Data from the USA displayed an equal proportion of students reporting the presence or absence of AI events in their curriculum (50% each). The residual 23 countries, encompassing Germany, Portugal, Mexico, Brazil, Poland, UAE, Austria, Italy, India, Argentina, Macedonia, Canada, Slovenia, Ecuador, Australia, Azerbaijan, Japan, Spain, Chile, Moldova, South Africa, Nepal, and Nigeria, had a lower proportion of students reporting the integration of AI in the medical curriculum. Abbreviations: UAE, United Arab Emirates; USA, United States of America
Fig. 3 Pie charts illustrating student responses at the country level for the item "As part of my studies, there are curricular events on artificial intelligence (AI) in medicine.". A more filled, darker red chart indicates a higher proportion of students reporting no AI events, while a less filled, greener chart indicates fewer students reporting the absence of AI events. The missing portion of each chart displays the proportion of students who reported AI events, regardless of the duration. An all-white pie chart indicates that all students reported AI events in the medical curriculum. The absolute number of responses per country is shown above each chart. Analysis of the pie charts from countries with a representative sample of at least 50 respondents reveals that, among 28 nations, only four (Indonesia, Switzerland, Vietnam, and China) exhibited over 50% of students reporting the inclusion of AI events within their medical curriculum. Data from the USA displayed an equal proportion of students reporting the presence or absence of AI events in their curriculum (50% each). The residual 23 countries, encompassing Germany, Portugal, Mexico, Brazil, Poland, UAE, Austria, Italy, India, Argentina, Macedonia, Canada, Slovenia, Ecuador, Australia, Azerbaijan, Japan, Spain, Chile, Moldova, South Africa, Nepal, and Nigeria, had a lower proportion of students reporting the integration of AI in the medical curriculum. Abbreviations: UAE, United Arab Emirates; USA, United States of AmericaCC BY · PubMed Central
Fig. 4 Gantt diagrams depicting medical students' preferences in AI diagnostics. a AI explainability (n = 3659, 80.2%) versus higher accuracy (n = 902, 19.8%) and b) higher sensitivity (n = 2906, 63.9%) versus higher specificity (n = 1118, 24.6%) or equal sensitivity and specificity (n = 524, 11.5%)
Fig. 4 Gantt diagrams depicting medical students' preferences in AI diagnostics. a AI explainability (n = 3659, 80.2%) versus higher accuracy (n = 902, 19.8%) and b) higher sensitivity (n = 2906, 63.9%) versus higher specificity (n = 1118, 24.6%) or equal sensitivity and specificity (n = 524, 11.5%)CC BY · PubMed Central

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Research works printed but not linked: 3

  1. Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019;5(1):e13930. · DOI · Paper page
  2. Schinkel K, Nannan M, Panday R, Car J. Introducing Artificial Intelligence Training in Medical Education. JMIR Med Educ. 2019;5(2):e16048. https://doi.org/10.2196/16048. · DOI · Paper page
  3. 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
  • Social Sciences › Education › Innovations in Medical Education
  • Social Sciences › Education › 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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