Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students
Open accessAbstract
Background: Artificial intelligence’s advancement in medicine and its worldwide implementation will be one of the main elements of medical education in the coming years. This study aimed to translate and psychometric evaluation of the Persian version of the medical artificial intelligence readiness scale for medical students. Methods: The questionnaire was translated according to a backward-forward translation procedure. Reliability was assessed by calculating Cronbach’s alpha coefficient. Confirmatory Factor Analysis was conducted on 302 medical students. Content validity was evaluated using the Content Validity Index and Content Validity Ratio. Results: The Cronbach’s alpha coefficient for the whole scale was found to be 0.94. The Content Validity Index was 0.92 and the Content Validity Ratio was 0.75. Confirmatory factor analysis revealed a fair fit for four factors: cognition, ability, vision, and ethics. Conclusion: The Persian version of the medical artificial intelligence readiness scale for medical students consisting of four factors including cognition, ability, vision, and ethics appears to be an almost valid and reliable instrument for the evaluation of medical artificial intelligence readiness.
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?
MAIRS-MS evaluates four domains of AI competencies and literacy: cognition, ability, vision, and ethics.
“This tool evaluates four domains of AI competencies and literacy: cognition, ability, vision, and ethics [35, 36].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [35]
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
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013) · Educational and Psychological Measurement · doi:10.1177/0013164413495237 · Cited by 3,931 · see citing works
Sousa, V. D., & Rojjanasrirat, W. (2011) · Journal of Evaluation in Clinical Practice · doi:10.1111/j.1365-2753.2010.01434.x · Cited by 3,322 · see citing works
Two Quantitative Approaches for Estimating Content Validity
Wynd, C. A., Schmidt, B., & Schaefer, M. A. (2003) · Western Journal of Nursing Research · doi:10.1177/0193945903252998 · Cited by 807 · see citing works
Recalculation of the Critical Values for Lawshe’s Content Validity Ratio
Wilson, F. R., Pan, W., & Schumsky, D. A. (2012) · Measurement and Evaluation in Counseling and Development · doi:10.1177/0748175612440286 · Cited by 703 · see citing works
Automated analysis of free speech predicts psychosis onset in high-risk youths
Bedi, G., Carrillo, F., Cecchi, G. A., Slezak, D. F., Sigman, M., Mota, N. B., Ribeiro, S., Javitt, D. C., Copelli, M., & Corcoran, C. M. (2015) · npj Schizophrenia · doi:10.1038/npjschz.2015.30 · Cited by 673 · 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
Sapci, A. H., & Sapci, H. A. (2020) · JMIR Medical Education · doi:10.2196/19285 · Cited by 377 · see citing works
Karaca, O., Çalışkan, S. A., & Demir, K. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02546-6 · Cited by 377 · see citing works
Will artificial intelligence solve the human resource crisis in healthcare?
Meskó, B., Hetényi, G., & Győrffy, Z. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3359-4 · Cited by 360 · see citing works
Kassahun, Y., Yu, B., Tibebu, A. T., Stoyanov, D., Giannarou, S., Metzen, J. H., & Vander Poorten, E. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1305-z · Cited by 238 · see citing works
Attitudes and perceptions of dental students towards artificial intelligence
Yüzbaşıoğlu, E. (2020) · Journal of Dental Education · doi:10.1002/jdd.12385 · Cited by 190 · see citing works
Koller, I., Levenson, M. R., & Glück, J. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00126 · Cited by 169 · see citing works
Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine
Park, C. J., Yi, P. H., & Siegel, E. L. (2021) · Current Problems in Diagnostic Radiology · doi:10.1067/j.cpradiol.2020.06.011 · Cited by 167 · 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
Where medical education meets artificial intelligence: ‘Does technology care?’
van der Niet, A. G., & Bleakley, A. (2020) · Medical Education · doi:10.1111/medu.14131 · Cited by 128 · see citing works
AI-RADS: an artificial intelligence curriculum for residents
Lindqwister, A. L., Hassanpour, S., Lewis, P. J., & Sin, J. M. (2021) · Academic Radiology · doi:10.1016/j.acra.2020.09.017 · Cited by 103 · see citing works
Siegersma, K. R., Leiner, T., Chew, D. P., Appelman, Y., Hofstra, L., & Verjans, J. W. H. (2019) · Netherlands Heart Journal · doi:10.1007/s12471-019-01311-1 · Cited by 97 · 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
Liu, D. S., Sawyer, J., Luna, A., Aoun, J., Wang, J., Boachie, L., Halabi, S., & Joe, B. (2022) · JMIR Medical Education · doi:10.2196/38325 · Cited by 90 · see citing works
Body pedagogics: embodied learning for the health professions
Kelly, M. A., Ellaway, R., Scherpbier, A. J. J. A., KING, N. M., & Dornan, T. (2019) · Medical Education · doi:10.1111/medu.13916 · Cited by 82 · see citing works
Binder, T., Sandmann, A., Sures, B., Friege, G., Theyßen, H., & Schmiemann, P. (2019) · International Journal of STEM Education · doi:10.1186/s40594-019-0189-9 · Cited by 69 · see citing works
Medical and Dental Professionals Readiness for Artificial Intelligence for Saudi Arabia Vision 2030
Aboalshamat, K. T., Alhuzali, R., Alalyani, A. J., Alsharif, S., Qadhi, H., Almatrafi, R. M., Ammash, D., & Alotaibi, S. (2022) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/nu8y6y6q1m · Cited by 68 · see citing works
Laupichler, M. C., Hadizadeh, D. R., Wintergerst, M. W. M., von der Emde, L., Paech, D., Dick, E. A., & Raupach, T. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03866-x · Cited by 62 · see citing works
Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs
Gray, K. M., Slavotinek, J., Dimaguila, G. L., & Choo, D. (2022) · JMIR Medical Education · doi:10.2196/35223 · Cited by 56 · see citing works
The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading
Hainc, N., Federau, C., Stieltjes, B., Blatow, M., Bink, A., & Stippich, C. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00489 · Cited by 52 · see citing works
Mota-Valtierra, G., Rodríguez‐Reséndiz, J., & Herrera-Ruíz, G. (2019) · Sustainability · doi:10.3390/su11174642 · Cited by 34 · see citing works
Singh, S. P., Anirvan, P., & Meher, D. (2021) · Euroasian Journal of Hepato-Gastroenterology · doi:10.5005/jp-journals-10018-1322 · Cited by 15 · see citing works
Cognitive Constructivist Way of Teaching Scientific and Technical Contents
Žuljan, D., Zuljan, M. V., & Papak, P. P. (2021) · International Journal of Cognitive Research in Science Engineering and Education · doi:10.23947/2334-8496-2021-9-1-23-36 · Cited by 8 · see citing works
Salajegheh, M., Hekmat, S. N., & Afshar, R. M. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04031-8 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [5]Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:160605718. 2016.
- [35]Pang Yi Xuan MIFF, Muhammad Imran bin Al Nazir, Hussain NTJ, Sujata Khobragade. Htoo Htoo Kyaw Soe, Soe Moe, Mila Nu Nu, Htay. Readiness towards Artificial Intelligence among Undergraduate Medical Students in Malaysia. Education in Medicine Journal. 2023.
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
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
“This tool evaluates four domains of AI competencies and literacy: cognition, ability, vision, and ethics [35, 36].”
- Authors
- Rezazadeh, H., Ahmadipour, H., & Salajegheh, M.
- Affiliations
- Kerman University of Medical Sciences (IR)
- Corresponding author
- Salajegheh, Mahla
- Venue
- BMC Medical Education
- Published
- 2023-07-24
- Volume
- 23
- Issue
- 1
- Pages
- 527-527
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC10367280
- OpenAlex
- W4385197478
- Cited by
- 22
- References
- 31
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 2
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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
- Social Sciences › Education › Health Education and Validation
- Keywords
- artificial intelligence in healthcare, medical students, Persian translation, scale validation, psychometric properties, AI ethics, AI literacy, computer vision
- MeSH terms
- Artificial Intelligence, Cognition, Humans, Psychometrics, Students, Medical, Reproducibility of Results
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
