Medical Students’ Knowledge and Attitude Towards Artificial Intelligence: An Online Survey
Open accessAbstract
Introduction: Within the near future, doctors can be anticipated to encounter patients in very distinctive wellbeing care settings compared with the present time. As a result, artificial intelligence will be an essential tool. Objectives: The purpose of this study is to investigate the attitudes of Jordanian medical students regarding Artificial Intelligence (AI) and Machine Learning (ML). Moreover, to estimate the level of knowledge and understanding of the effects of AI on medical students. Methods: Nine hundred medical students from six universities in Jordan participated in this survey. The participants were asked to fill out an electronic pre-validated questionnaire using Google’s forms and those forms were published via social media. The questionnaire included questions of Likert and dichotomous questions. Results: 89% of the students believed in the importance of AI in the medical field, and 71.4% believed in the beneficiary of teaching AI in the medical career. 47% of the students had an understanding of the basic principles of AI, 68.4% of the students believed that it is mandatory for medical students to receive knowledge of AI. Statistically, students who received teaching/training in AI were more likely to consider radiology as a career given the advancement in AI (p = 0.000). Conclusion: Medical students in Jordanian universities appreciate the importance of artificial intelligence and machine learning in medical advancements. Adding courses and training related to artificial intelligence and machine learning to the study plan should be considered.
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?
In Jordan, only 18.4% of respondents reported feeling competent to apply AI practically.
“Only 31% of respondents in Malaysia and 18.4% in Jordan reported feeling competent to apply AI practically [44, 48].”
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 [44]
The Results report that 18.4% of Jordanian students had received AI teaching or training, not that they felt competent to apply AI. For the closer question—whether they possessed the knowledge needed to work with AI in routine clinical practice after graduation—the Results report 54% agreeing or strongly agreeing. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured616 out of 900 students (68.4%) considered AI education mandatory.
“616 out of 900 students (68.4%) considering AI education mandatory [44]”
In Malaysia, only 31% of respondents reported feeling competent to apply AI practically.
“Only 31% of respondents in Malaysia and 18.4% in Jordan reported feeling competent to apply AI practically [44, 48].”
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 [44]
The Results (page unavailable) report that 54% of respondents felt they would have the knowledge to work with AI in routine clinical practice after graduation; this study surveyed students in Jordan, not Malaysia. A separate passage reports 18.4% received AI teaching or training, not that they felt competent. Our copy provides no finding about the claimed 31% of Malaysian respondents. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIn developing regions, including the Middle East, AI integration remains limited due to disparities in infrastructure, institutional readiness, and faculty expertise.
“However, in developing regions, including the Middle East, AI integration remains limited due to disparities in infrastructure, institutional readiness, and faculty expertise [6–8].”
From Knowledge, attitudes, and practices toward AI technology (ChatGPT) among nursing students at Palestinian universities (2025) · cites this paper as [6]
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
Artificial intelligence and deep learning in ophthalmology
Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2018-313173 · Cited by 1,554
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works
Overview of artificial intelligence in medicine
Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_440_19 · Cited by 1,106 · 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
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
Artificial intelligence in medicine: current trends and future possibilities
Buch, V., Ahmed, I., & Maruthappu, M. (2018) · British Journal of General Practice · doi:10.3399/bjgp18x695213 · Cited by 446 · 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
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
Will Artificial Intelligence Replace Radiologists?
Langlotz, C. P. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019190058 · Cited by 246 · see citing works
Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review
Hogarty, D. T., Su, J. C., Phan, K. H., Attia, M., Hossny, M., Nahavandi, S., Lenane, P., Moloney, F. J., & Yazdabadi, A. (2019) · American Journal of Clinical Dermatology · doi:10.1007/s40257-019-00462-6 · Cited by 186 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Department of Health and Social Care. The Topol review: preparing
Cited by
Sorted by citations
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · see citing works
Mousavi Baigi, S. F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., & Kimiafar, K. (2023) · Health Science Reports · doi:10.1002/hsr2.1138 · Cited by 255 · see citing works
Investigating Students’ Perceptions towards Artificial Intelligence in Medical Education
Buabbas, A. J., Miskin, B., Alnaqi, A. A., Ayed, A. K., Shehab, A. A., Syed-Abdul, S., & Uddin, M. (2023) · Healthcare · doi:10.3390/healthcare11091298 · Cited by 152 · see citing works
Students’ attitudes towards using ChatGPT as a learning tool: the case of the University of Jordan.
O. Ajlouni, A., Abd-Alkareem Wahba, F., & Salem Almahaireh, A. (2023) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v17i18.41753 · Cited by 116 · see citing works
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., et al. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06035-4 · Cited by 108 · see citing works
Doumat, G., Daher, D., Ghanem, N.-N., & Khater, B. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1015418 · Cited by 91 · see citing works
Pokrivcakova, S. (2023) · Journal of language and cultural education · doi:10.2478/jolace-2023-0031 · Cited by 81 · see citing works
Mishra, T., Sutanto, E., Rossanti, R., Pant, N., Ashraf, A., Raut, A., Uwabareze, G., Oluwatomiwa, A., & Zeeshan, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-81370-6 · Cited by 54 · see citing works
Salama, N., Bsharat, R., Alwawi, A., & Khlaif, Z. N. (2025) · BMC Nursing · doi:10.1186/s12912-025-02913-4 · Cited by 42 · see citing works
Alwadani, F., Lone, A., Hakami, M., Moria, A., Alamer, W., Alghirash, R., Alnawah, A., & Hadadi, A. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s455260 · Cited by 30 · 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
“%) considering AI education mandatory [44], 4,267 out of 4,492 participants (95”
Influential
- Authors
- Al Saad, M. M., Shehadeh, A., Alanazi, S., Alenezi, M., Abu alez, A., Eid, H., Alfaouri, M. S., Aldawsari, S., & Alenezi, R.
- Affiliations
- University of Jordan (JO)
- Venue
- The Open Public Health Journal
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2022-05-24
- Volume
- 15
- Issue
- 1
- ISSN
- 1874-9445 (ISSN-L)
- OpenAlex
- W4281487897
- Cited by
- 60
- References
- 10
- Citations by year
- 2026: 16 · 2025: 14 · 2024: 17 · 2023: 12 · 2022: 1
- FWCI
- 3.31
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The Open Public Health Journal (publishedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence, machine learning, medical students, knowledge and attitudes, artificial intelligence in education, medical curriculum
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
