German medical students´ views regarding artificial intelligence in medicine: A cross-sectional survey
Open accessAbstract
Background: Medical students will likely be most impacted by the envisaged move to artificial intelligence (AI) driven digital medicine, and there is a need to better understand their views regarding the use of AI technology in medicine. This study aimed to explore German medical students´ views about AI in medicine. Methods: A cross-sectional survey was conducted in October 2019 with all new medical students at the Ludwig Maximilian University of Munich and the Technical University Munich. This represented approximately 10% of all new medical students in Germany. Results: A total of 844 medical students participated (91.9% response rate). Two thirds (64.4%) did not feel well informed about AI in medicine. Just over a half (57.4%) of students thought that AI has useful applications in medicine, particularly in drug research and development (82.5%), less so for clinical uses. Male students were more likely to agree with advantages of AI, and female participants were more likely to be concerned about disadvantages. The vast majority of students thought that when AI is used in medicine that it is important that there are legal rules regarding liability (97%) and oversight mechanisms (93.7%), that physicians should be consulted prior to implementation (96.8%), that developers should be able to explain to them the details of the algorithm (95.6%), that algorithms should use representative data (93.9%), and that patients should always be informed when AI is used (93.5%). Conclusions: Medical schools and continuing medical education organisers need to promptly develop programs to ensure that clinicians are able to fully realize the potential of AI technology. It is also important that legal rules and oversight are implemented to ensure that future clinicians are not faced with a workplace where important issues around responsibility are not clearly regulated.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A lack of workplace responsibility is a concern, with some experts calling for legislation and oversight processes to prevent this.
“Alternatively, a lack of workplace responsibility is another concern, with some experts calling for legislation and oversight processes to prevent this [167].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [167]
Universities from the United States, Canada, France, Germany, and Taiwan have integrated AI into their UGME curricula through various courses and strategies.
“Universities from the United States, Canada, France, Germany, and Taiwan have integrated AI into their UGME curricula through various courses and strategies, providing valuable feedback for other medical schools to observe how their implementation efforts were carried out [167].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [167]
The Discussion says AI training programs or modules in medical schools are currently limited and recommends developing them; it does not identify universities in the five named countries that have integrated AI into undergraduate medical education. The Survey implementation section describes a compulsory course at two Munich universities but explicitly says it has no specific focus on AI. Searches for the named countries and for AI curricula or courses found no account of the claimed implementations or feedback from them; page numbers were unavailable in our copy. (LLM, conf=0.73)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · see citing works
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · see citing works
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · see citing works
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883 · see citing works
Progress in evidence-based medicine: a quarter century on
Djulbegovic, B., & Guyatt, G. H. (2017) · The Lancet · doi:10.1016/s0140-6736(16)31592-6 · Cited by 1,022 · see citing works
(2019) · Cited by 986 · see citing works
Clinical decision support in the era of artificial intelligence
Shortliffe, E. H., & Sepúlveda, M. J. (2018) · JAMA · doi:10.1001/jama.2018.17163 · Cited by 920 · see citing works
The ethics of artificial intelligence
Bostrom, N., & Yudkowsky, E. (2014) · The Cambridge Handbook of Artificial Intelligence · doi:10.1017/cbo9781139046855.020 · Cited by 918 · see citing works
Machine learning in medicine: Addressing ethical challenges
Vayena, E., Blasimme, A., & Cohen, I. G. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002689 · Cited by 912 · see citing works
Fiske, A., Henningsen, P., & Buyx, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13216 · Cited by 857 · see citing works
Self-citationMedical 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
How artificial intelligence is changing drug discovery
Fleming, N. (2018) · Nature · doi:10.1038/d41586-018-05267-x · Cited by 546 · cited 1× · see citing works
“With the ability to learn from large sets of clinical data, medical AI applications have the potential to support a range of activities, including diagnosis [4], clinical decision making [5], personalized medicine [6], therapy [7], clinical research [8], drug development [9], and administrative processes [10].”
BackgroundArtificial Intelligence and Personalized Medicine
Schork, N. J. (2019) · Cancer treatment and research · doi:10.1007/978-3-030-16391-4_11 · Cited by 518 · see citing works
Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., & Poon, D. S. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0830-7 · Cited by 507 · see citing works
Faden, R. R., Kass, N. E., Goodman, S. N., Pronovost, P., Tunis, S., & Beauchamp, T. L. (2013) · Hastings Center Report · doi:10.1002/hast.134 · Cited by 498 · see citing works
Robot Therapy: A New Approach for Mental Healthcare of the Elderly – A Mini-Review
Shibata, T., & Wada, K. (2011) · Gerontology · doi:10.1159/000319015 · Cited by 437 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · PhilPapers (PhilPapers Foundation) · Cited by 394 · 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
Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019) · Journal of Medical Internet Research · doi:10.2196/12802 · Cited by 346 · see citing works
Ethical dimensions of using artificial intelligence in health care
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.121 · Cited by 328 · see citing works
Gong, B., Nugent, J. P., Guest, W., Parker, W., Chang, P. J., Khosa, F., & Nicolaou, S. (2019) · Academic Radiology · doi:10.1016/j.acra.2018.10.007 · Cited by 325 · see citing works
Grosz, B. J., Grant, D. G., Vredenburgh, K., Behrends, J., Hu, L. J., Simmons, A., & Waldo, J. (2019) · Communications of the ACM · doi:10.1145/3330794 · Cited by 249 · 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
Health care robotics: qualitative exploration of key challenges and future directions
Cresswell, K., Cunningham-Burley, S., & Sheikh, A. (2018) · Journal of Medical Internet Research · doi:10.2196/10410 · Cited by 220 · see citing works
Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries
Gerke, S., Stern, A. D., & Minssen, T. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0306-7 · Cited by 178 · see citing works
An embedded ethics approach for AI development
McLennan, S., Fiske, A., Celi, L. A., Müller, R., Harder, J., Ritt, K., Haddadin, S., & Buyx, A. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0214-1 · Cited by 133 · see citing works
Self-citationAn AI boost for clinical trials
Woo, M. (2019) · Nature · doi:10.1038/d41586-019-02871-3 · Cited by 131 · see citing works
van Hoek, J., Huber, A. T., Leichtle, A. B., Härmä, K., Hilt, D., von Tengg‐Kobligk, H., Heverhagen, J. T., & Poellinger, A. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108742 · Cited by 124 · see citing works
Artificial intelligence : a European perspective
(2018) · EUR · doi:10.2760/936974 · Cited by 111 · see citing works
Responsible AI and moral responsibility: a common appreciation
Tigard, D. W. (2020) · AI and Ethics · doi:10.1007/s43681-020-00009-0 · Cited by 67 · see citing works
The double-edged sword of digital self-care: Physician perspectives from Northern Germany
Fiske, A., Buyx, A., & Prainsack, B. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113174 · Cited by 65 · see citing works
Self-citationArtificial Intelligence: Some ethical issues and regulatory challenges
Coeckelbergh, M. (2021) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.26116/techreg.2019.003 · Cited by 62 · see citing works
An awakening in medicine: the partnership of humanity and intelligent machines
Celi, L. A., Fine, B., & Stone, D. J. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30127-x · Cited by 45 · see citing works
Artificial Intelligence and the Augmentation of Health CareDecision-Making
Davenport, T. H., & Glover, W. J. (2018) · NEJM Catalyst · Cited by 18 · see citing works
Embedded EthiCS: Integrating Ethics Broadly Across Computer Science EducationPreprint
Grosz, B. J., Grant, D. G., Vredenburgh, K., Behrends, J., Hu, L. J., Simmons, A., & Waldo, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.05686 · Cited by 15 · see citing works
Kuhn, E. M., Rogge, A., Schreyer, K. F., & Buyx, A. (2021) · Das Gesundheitswesen · doi:10.1055/a-1473-5655 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [12]Johannes MS, et al. An overview of the developmental process for the modular prosthetic limb. Johns Hopkins APL Technical Digest 2011; 30: 207– 216.
Cited by
Sorted by citations
Weidener, L., & Fischer, M. (2024) · JMIR Medical Education · doi:10.2196/51247 · Cited by 163 · see citing works
Amiri, H., Peiravi, S., rezazadeh shojaee, S. S., Rouhparvarzamin, M., Nateghi, M. N., Etemadi, M. H., ShojaeiBaghini, M., Musaie, F., Anvari, M. H., & Asadi Anar, M. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05406-1 · Cited by 149 · see citing works
“The study design of 19 studies was cross-sectional [20–38], and the rest followed a mixed methodology [18, 19, 39].”
BackgroundOmari, O. A., Alshammari, M., Jabri, W. A., Yahyaei, A. A., Aljohani, K. A., Sanad, H. M., Al‐Jubouri, M. B., Bashayreh, I., Fawaz, M. A., ALBashtawy, M., et al. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06452-5 · Cited by 84 · see citing works
Jebreen, K., Radwan, E., Kammoun-Rebai, W., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05465-4 · Cited by 63 · see citing works
Medical Students’ Perspectives on Trust in Medical AI: A Quantitative Comparative Study
Kajanová, J., & Badrov, A. (2024) · Asian Journal of Ethics in Health and Medicine · doi:10.51847/36mpdz9az8 · Cited by 58 · see citing works
Aldhafeeri, F. M. (2025) · Diagnostics · doi:10.3390/diagnostics15182300 · Cited by 61 · see citing works
Artificial 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
“Universities from the United States, Canada, France, Germany, and Taiwan have integrated AI into their UGME curricula through various courses and strategies, providing valuable feedback for other medical schools to observe how their implementation efforts were carried out [167].”
Using ChatGPT for medical education: the technical perspective
Chan, K. Y., Yuen, T. H., & Co, M. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06785-9 · Cited by 34 · see citing works
Rjoop, A., Alqudah, M. A., Alkhasawneh, R., Bataineh, N. M., Abdaljaleel, M., Rjoub, M. A., Alkhateeb, M., Abdelraheem, M., Al-Omari, S., Bani-Mari, O., et al. (2025) · JMIR Medical Education · doi:10.2196/62669 · Cited by 34 · see citing works
Allam, R. M., Abdelfatah, D., Khalil, M. I. M., Elsaieed, M. M., & Desouky, E. D. E. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06201-8 · Cited by 35 · see citing works
Sharma, V., Saini, U., Pareek, V., Sharma, L., & Kumar, S. (2023) · Scripta Medica · doi:10.5937/scriptamed54-46267 · Cited by 27 · see citing works
Seneviratne, T., Kodikara, K. G., Abeykoon, I., & Palpola, W. (2025) · JMIR Medical Education · doi:10.2196/73798 · Cited by 24 · see citing works
Understanding AI in Healthcare: Perspectives of Future Healthcare Professionals
Sorte, S. R., Rawekar, A., & Rathod, S. B. (2024) · Cureus · doi:10.7759/cureus.66285 · Cited by 16 · see citing works
Pawelczyk, J., Kraus, M., Eckl, L., Nehrer, S., Aurich, M. E., Izadpanah, K., Siebenlist, S., & Rupp, M. (2024) · Archives of Orthopaedic and Trauma Surgery · doi:10.1007/s00402-024-05408-0 · Cited by 17 · see citing works
Fritz, B., Eppelmann, L., Edelmann, A., Rohrmann, S., & Wessa, M. (2025) · BMC Psychology · doi:10.1186/s40359-025-02954-z · Cited by 15 · see citing works
Assessing digital competencies and AI ethics awareness among customers in the banking sector
Fundira, M., Edoun, E. I., Pradhan, A., & Mbohwa, C. (2024) · African Journal of Science Technology Innovation and Development · doi:10.1080/20421338.2024.2388619 · Cited by 14 · see citing works
Considerations for the application of artificial intelligence in vascular surgical education
Rigberg, D. A., & Jim, J. (2023) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2023.07.004 · Cited by 12 · see citing works
Malešević, A., Kolesárová, M., & Čartolovni, A. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01092-2 · Cited by 10 · see citing works
Ankamah, S., Gyesi, K., & Amponsah, V. (2024) · Information Development · doi:10.1177/02666669241283790 · Cited by 8 · see citing works
Jaber, S. A., Hasan, H. E., Alzoubi, K. H., & Khabour, O. F. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41331 · Cited by 9 · see citing works
Curiello, S., Iannuzzi, E., Meissner, D., & Nigro, C. (2025) · European Journal of Innovation Management · doi:10.1108/ejim-01-2024-0078 · Cited by 7 · see citing works
Perceptions of Medical Students towards Artificial Intelligence
Rizwan, S., Rizwan, S., Rizwan, M., Hashim, A., Nawal, & Batool, S. (2025) · Pakistan Journal of Health Sciences · doi:10.54393/pjhs.v6i1.2364 · Cited by 6 · see citing works
Berrami, H., Barkouk, A., Belayachi, N., Jallal, M., Serhier, Z., & Othmani, M. B. (2024) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc62074.2024.10616755 · Cited by 5 · see citing works
Al-Kahtani, N. K., Subbarayalu, A. V., Raman, V., Jamjoom, M. M., Al-Ansary, N. F. S., Alakrawi, Z., AL-Ibrahim, R. J., & Al-Zaki, W. A. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101710 · Cited by 4 · see citing works
Integration of AI-based applications in education: how students feel about the ChatGPT era?
Shubha, T. A., Vaidya, P. G., Ali, P. M. N., & Lund, B. D. (2025) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-04-2024-0227 · Cited by 4 · see citing works
Micallef Grimaud, L., & Gathu, C. (2024) · doi:10.2196/preprints.57746 · Cited by 3 · see citing works
Hawal, M. V., Simon, M., Husain, N. H. A., Al-Harrasi, S. K. R., & Al-Hinai, L. N. S. (2025) · Journal of Medical Education Development · doi:10.61882/edcj.18.3.14 · Cited by 3 · see citing works
Sirajudeen, N., Bhatt, N., Patel, A., Fallouh, G., Hyclová, A., Varadpande, M., Goel, A. R., Rajaratnam, W., & Dost, S. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08319-9 · Cited by 3 · see citing works
Medical Students’ Perceptions and Attitude Toward the Use of Artificial Intelligence in Healthcare
Albusmait, A., AlSaleh, A. F. F., & Ahmed, J. (2026) · Advances in Medical Education and Practice · doi:10.2147/amep.s541284 · Cited by 3 · see citing works
Simoni, J., Mengual, E., Aschenbrenner-Noriega, I., Monforte-Martínez, C., Pérez-Merino, M., Sawczyn, G., Zurita, R., & Pereira, J. L. (2026) · MedEdPublish · doi:10.12688/mep.21319.2 · Cited by 3 · see citing works
Grimaud, L. M., & Gathu, C. (2024) · JMIR Medical Education · doi:10.2196/57746 · Cited by 4 · see citing works
Perceptions, Attitudes, and Use of AI by Medical Students: Mixed Methods Study
Paris, F., Garrouste, V., & Vuillaume, L. A. (2026) · JMIR Medical Education · doi:10.2196/91345 · Cited by 2 · see citing works
Artificial intelligence (AI) in the medical consultation: Friend or foe?
Chirumbolo, S., Franzini, M., & Tirelli, U. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105227 · Cited by 1 · see citing works
Current Situation and Prospects of Education in Medical Imaging Artificial Intelligence
Zhang, X., Jiang, L. H., Liang, M., Guo, L., Xiao, Y., & Wang, Y. (2024) · Springer eBooks · doi:10.1007/978-981-99-8441-1_28
Readiness of Medical Students in China to AI implementation in Clinical practice (Preprint)Preprint
AlGerafi, M. A. M., AlBeshari, S., Zhou, Y., Alfadda, H. A., & Mahyoub, M. A. (2024) · doi:10.2196/preprints.70513
Huang, Z., Tang, J., Wang, P., & Feng, X. (2025) · doi:10.2196/preprints.80171
Giavina-Bianchi, B., Bianchi, M. G., Wolosker, N., Amaro, E., & Machado, B. S. (2025) · Einstein (São Paulo) · doi:10.31744/einstein_journal/2025ao1401
Amiri, H., Peiravi, S., Shojaee, S. S. R., Rouhparvarzamin, M., Nateghi, M. N., Etemadi, M. H., ShojaeiBaghini, M., Musaie, F., Anvari, M. H., & Anar, M. A. (2024) · Figshare · doi:10.6084/m9.figshare.26706867
Jebreen, K., RADWAN, E. S., Rebai, W. K., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · Figshare · doi:10.6084/m9.figshare.26717358
Jebreen, K., RADWAN, E. S., Rebai, W. K., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · Figshare · doi:10.6084/m9.figshare.26717355
Jebreen, K., RADWAN, E. S., Rebai, W. K., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · Figshare · doi:10.6084/m9.figshare.25770556
Jebreen, K., RADWAN, E. S., Rebai, W. K., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · Figshare · doi:10.6084/m9.figshare.25770366
Simoni, J., Mengual, E., Aschenbrenner-Noriega, I., Monforte-Martínez, C., Pérez-Merino, M., Sawczyn, G., Zurita, R., & Pereira, J. L. (2025) · MedEdPublish · doi:10.12688/mep.21319.1 · Cited by 5 · see citing works
Borzu, Z. A., Keikha, L., & Shahraki-Mohammadi, A. (2025) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205251407758 · Cited by 1 · see citing works
Paris, F., & Vuillaume, L. A. (2026) · doi:10.2196/preprints.91345
Zahn, A., Mücke, M., Sellin, J., & Ateş, G. (2026) · Medical Education Online · doi:10.1080/10872981.2026.2639835
Zhang, W., Han, J., Han, X., Xu, H., Wang, L., Lin, T., Tan, P., Peng, Z., & Zheng, X. (2026) · JMIR Medical Education · doi:10.2196/89411 · Cited by 1 · see citing works
A Radiology Workflow Intelligence Mesh for AI-Embedded Diagnostic Operations
Amrani, F. Z., & Benali, Y. (2023) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/q798488740
Chenfouh, I., Abusheikha, M., Benhadi, O., Fahd, O. E., Elomri, I., & Aji, N. (2026) · Pan African Medical Journal · doi:10.11604/pamj.2026.55.4.50412
Chenfouh, I., Abusheikha, M., Benhadi, O., Fahd, O. E., Elomri, I., & Aji, N. (2026) · Pan African Medical Journal · doi:10.11604/pamj.2026.55.2.53768
- Authors
- McLennan, S., Meyer, A., Schreyer, K., & Buyx, A.
- Affiliations
- Technical University of Munich (DE)
- University of Basel (CH)
- Corresponding author
- McLennan, Stuart
- Venue
- PLOS Digital Health
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-10-04
- Volume
- 1
- Issue
- 10
- Pages
- e0000114
- ISSN
- 2767-3170 (ISSN-L)
- PMC
- PMC9931368
- OpenAlex
- W4301390460
- Cited by
- 50
- References
- 38
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 21 · 2023: 4
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLOS Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, medical students, attitudes toward AI, digital medicine, liability, algorithmic transparency, informed consent, gender differences, medical education, pharmaceutical R&D
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
