Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects
Open accessPublished version of a preprint
Abstract
Background: The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present. Objective: This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT. Methods: This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students’ perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate. Results: Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant. Conclusions: This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
Haenlein, M., & Kaplan, A. (2019) · California Management Review · doi:10.1177/0008125619864925 · Cited by 2,655 · see citing works
Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023) · JMIR Medical Education · doi:10.2196/45312 · Cited by 2,078 · see citing works
Dave, T., Athaluri, S. A., & Singh, S. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1169595 · Cited by 1,204 · see citing works
Medical students' attitude towards artificial intelligence: a multicentre survey
Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754 · see citing works
On Defining Artificial Intelligence
Wang, P. (2019) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2019-0002 · Cited by 740 · see citing works
Introducing artificial intelligence training in medical education
Paranjape, K., Schinkel, M., Nannan Panday, R., Car, J., & Nanayakkara, P. (2019) · JMIR Medical Education · doi:10.2196/16048 · Cited by 634 · see citing works
Artificial Intelligence in Health Care: A Report From the National Academy of Medicine
Matheny, M. E., Whicher, D., & Thadaney Israni, S. (2020) · JAMA · doi:10.1001/jama.2019.21579 · Cited by 628 · see citing works
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0645-y · Cited by 479 · see citing works
Artificial intelligence in medical education
Masters, K. (2019) · Medical Teacher · doi:10.1080/0142159x.2019.1595557 · Cited by 469 · see citing works
Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N. H., Peng, L. H., Hipp, J., & Stumpe, M. C. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0147-oa · Cited by 402 · see citing works
Physician Confidence in Artificial Intelligence: An Online Mobile Survey
Oh, S., Kim, J. H., Choi, S.-W., Lee, H. J., Hong, J., & Kwon, S. H. (2019) · Journal of Medical Internet Research · doi:10.2196/12422 · Cited by 387 · see citing works
Lecler, A., Duron, L., & Soyer, P. (2023) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2023.02.003 · Cited by 385 · see citing works
Artificial intelligence in medical education: a cross-sectional needs assessment
Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03852-3 · Cited by 363 · see citing works
Artificial Intelligence in Undergraduate Medical Education: A Scoping Review
Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. (. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004291 · Cited by 324 · see citing works
The future of medical education and research: Is ChatGPT a blessing or blight in disguise?
Arif, T. B., Munaf, U., & Ul-Haque, I. (2023) · Medical Education Online · doi:10.1080/10872981.2023.2181052 · Cited by 317 · see citing works
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019) · Radiology · doi:10.1148/radiol.2018180694 · Cited by 294 · see citing works
What do medical students actually need to know about artificial intelligence?
McCoy, L. G., Nagaraj, S., Morgado, F., Harish, V., Das, S., & Celi, L. A. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0294-7 · Cited by 240 · see citing works
Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study
Sallam, M., Salim, N. A., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D., Hallit, R., & Hallit, S. (2023) · JMIR Medical Education · doi:10.2196/48254 · Cited by 234 · cited 1× · see citing works
“A study conducted with health students found that only 11.3% (55/458) of respondents reported using the ChatGPT, a rate considerably lower than the findings of this study [29].”
ResultInfluentialMehta, N., Harish, V., Bilimoria, K., Morgado, F., Ginsburg, S., Law, M., & Das, S. (2021) · MedEdPublish · doi:10.15694/mep.2021.000075.1 · Cited by 145 · see citing works
Jha, N., Shankar, P. R., Al-Betar, M. A., Mukhia, R., Hada, K., & Palaian, S. (2022) · Advances in Medical Education and Practice · doi:10.2147/amep.s368519 · Cited by 143 · see citing works
Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices
Weidener, L., & Fischer, M. (2023) · Perspectives on Medical Education · doi:10.5334/pme.954 · Cited by 133 · cited 1× · see citing works
“Medical students perceived all 8 proposed ethical AI topics as highly relevant, which were recommended as potential teaching content for AI ethics in the current literature [37-39].”
BackgroundSelf-citationThe need for health AI ethics in medical school education
Katznelson, G., & Gerke, S. (2021) · Advances in Health Sciences Education · doi:10.1007/s10459-021-10040-3 · Cited by 125 · see citing works
ChatGPT in medical school: how successful is AI in progress testing?
Friederichs, H., Friederichs, W. J., & März, M. (2023) · Medical Education Online · doi:10.1080/10872981.2023.2220920 · Cited by 122 · see citing works
Applications of large language models in cancer care: current evidence and future perspectives
Iannantuono, G. M., Bracken-Clarke, D., Floudas, C. S., Roselli, M., Gulley, J. L., & Karzai, F. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1268915 · Cited by 115 · see citing works
Artificial Intelligence A Modern Approach (4th Edition)
Faisal, F. (2021) · Cited by 99 · see citing works
(2021) · Springer eBooks · doi:10.1007/978-3-030-58721-5 · Cited by 59 · see citing works
ChatGPT: temptations of progress
Doshi, R. H., Bajaj, S. S., & Krumholz, H. M. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2180110 · Cited by 58 · see citing works
McLennan, S., Meyer, A., Schreyer, K., & Buyx, A. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000114 · Cited by 50 · see citing works
A “fit for purpose” framework for medical education accreditation system design
Taber, S., Akdemir, N., Gorman, L., van Zanten, M., & Frank, J. (2020) · BMC Medical Education · doi:10.1186/s12909-020-02122-4 · Cited by 48 · see citing works
Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education
Quinn, T. P., & Coghlan, S. (2021) · arXiv · doi:10.48550/arxiv.2109.02866 · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Rony, M. K. K., Numan, S. M., Akter, K., Tushar, H., Debnath, M., Johra, F. T., Akter, F., Mondal, S., Das, M., Uddin, M. J., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36702 · Cited by 93 · see citing works
Exploring the Usage of ChatGPT Among Medical Students in the United States
Zhang, J. S., Yoon, C., Williams, D. K. A., & Pinkas, A. (2024) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205241264695 · Cited by 75 · see citing works
Issa, W. B., Shorbagi, A., Al-Sharman, A., Rababa, M., Al-Majeed, K., Radwan, H., Refaat Ahmed, F., Al-Yateem, N., Mottershead, R., Abdelrahim, D. N., et al. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06076-9 · Cited by 78 · see citing works
“Our participants also expressed positive attitudes toward the integration of AI concepts in HPE curricula, which was consistent with previous studies [21, 27, 30, 35, 36].”
BackgroundResultInfluentialArtificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62 · see citing works
“While many of the overviews and perspectives referred to ethical considerations concerning the use of AI in medical education [77], 15 publications focused explicitly on this topic [7, 38, 84, 97, 186, 190–199].”
Elhassan, S. E., Sajid, M. R., Syed, A. M., Fathima, S. A., Khan, B. S., & Tamim, H. (2025) · JMIR Medical Education · doi:10.2196/63065 · Cited by 39 · see citing works
Thomae, A. V., Witt, C. M., & Barth, J. (2024) · JMIR Medical Education · doi:10.2196/50545 · Cited by 36 · see citing works
McCoy, L., Ganesan, N., Rajagopalan, V., McKell, D., Niño, D. F., & Swaim, M. C. (2025) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205251339226 · Cited by 30 · see citing works
Ajalo, E., Mukunya, D., Nantale, R., Kayemba, F., Pangholi, K., Babuya, J., Langoya Akuu, S., Namiiro, A. M., Nsubuga, Y. B., Mpagi, J. L., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0313776 · Cited by 29 · see citing works
Tortella, F., Palese, A., Turolla, A., Castellini, G., Pillastrini, P., Landuzzi, M. G., Cook, C., Galeoto, G., Giovannico, G., Rodeghiero, L., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07176-w · Cited by 28 · see citing works
“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…”
InfluentialQuantum leap in medical mentorship: exploring ChatGPT’s transition from textbooks to terabytes
Chokkakula, S., Chong, S., Yang, B., Jiang, H., Yu, J., Han, R., Attitalla, I. H., Yin, C., & Zhang, S. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1517981 · Cited by 12 · 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
“…[24, 30] Continuous Improvement Update curricula regularly to reflect advances in AI and implement adaptable educational strategies [40, 51] Regional Considerations Adapt AI curricula to local healthcare needs, as demonstrated in Syria, Malaysia, Oman, and Nepal [36–48] To identify…”
Influential
- Authors
- Weidener, L., & Fischer, M.
- Affiliations
- UMIT TIROL - Private University for Health Sciences and Health Technology (AT)
- Corresponding author
- Weidener, Lukas
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2024-01-05
- Volume
- 10
- Pages
- e51247
- ISSN
- 2369-3762 (ISSN-L)
- OpenAlex
- W4390631601
- Cited by
- 163
- References
- 33
- Citations by year
- 2026: 73 · 2025: 64 · 2024: 26
- FWCI
- 15.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- 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, AI ethics education, medical education, ChatGPT, large language models, student perceptions, Germany, Austria, Switzerland
- MeSH terms
- Artificial Intelligence, Choline O-Acetyltransferase, Cross-Sectional Studies, Educational Status, Humans, Medicine, Students, Medical
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 ›
Are you an author, or do you see a problem with this page? Request a review
