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Sobhanian, Pooria, Eslami, Sara, Ghezel, Mohammad Amin, Nikbakht, Roya, Noaparast, Zohreh, Rezai, Mohammad Sadegh. (1404). Assessment of the Iranian Medical Students’ Attitudes and Readiness Toward Artificial Intelligence: A Cross-Sectional Study. سامانه مدیریت نشریات علمی, (), -. doi: 10.30476/ijvlms.2026.108645.1354
Pooria Sobhanian; Sara Eslami; Mohammad Amin Ghezel; Roya Nikbakht; Zohreh Noaparast; Mohammad Sadegh Rezai. "Assessment of the Iranian Medical Students’ Attitudes and Readiness Toward Artificial Intelligence: A Cross-Sectional Study". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.30476/ijvlms.2026.108645.1354
Sobhanian, Pooria, Eslami, Sara, Ghezel, Mohammad Amin, Nikbakht, Roya, Noaparast, Zohreh, Rezai, Mohammad Sadegh. (1404). 'Assessment of the Iranian Medical Students’ Attitudes and Readiness Toward Artificial Intelligence: A Cross-Sectional Study', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.30476/ijvlms.2026.108645.1354
Sobhanian, Pooria, Eslami, Sara, Ghezel, Mohammad Amin, Nikbakht, Roya, Noaparast, Zohreh, Rezai, Mohammad Sadegh. Assessment of the Iranian Medical Students’ Attitudes and Readiness Toward Artificial Intelligence: A Cross-Sectional Study. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.30476/ijvlms.2026.108645.1354

Assessment of the Iranian Medical Students’ Attitudes and Readiness Toward Artificial Intelligence: A Cross-Sectional Study

Interdisciplinary Journal of Virtual Learning in Medical Sciences
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 اسفند 1404    اصل مقاله (1.44 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30476/ijvlms.2026.108645.1354
نویسندگان
Pooria Sobhanian1؛ Sara Eslami* 1؛ Mohammad Amin Ghezel2؛ Roya Nikbakht3؛ Zohreh Noaparast4؛ Mohammad Sadegh Rezai5
1Student Research Committee, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
2Student Research Committee, Babol University of Medical Sciences, Babol, Iran
3Department of Biostatistics and Epidemiology, Faculty of Health, Mazandaran University of Medical Sciences, Sari, Iran
4Department of Radiopharmacy, Faculty of Pharmacy, Pharmaceutical Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran
5Pediatric Infectious Diseases Research Center, Communicable Diseases Institute, Mazandaran University of Medical Sciences, Sari, Iran
چکیده
Background: Artificial Intelligence (AI) is increasingly reshaping healthcare through advancements in diagnosis, therapeutic decision-making, and patient care management. This study designed to evaluate the attitudes and readiness of medical science students toward AI, to inform and improve AI-related educational programs.
Methods: This cross-sectional study was carried out at Mazandaran University of Medical Sciences (MAZUMS), Iran, between December 2023 and March 2024. A total of 637 students enrolled in medical and health sciences programs were recruited through convenience sampling via online platforms. Data were collected using a 31-item online questionnaire captured demographic information, exposure to AI-related education, and readiness for AI. The study utilized the validated Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). Statistical analyses were performed using SPSS version 22, with a significance threshold set at p < 0.05.
Results: In total, 637 Iranian medical sciences students participated. Of these, 54.2% were in preclinical training and 45.8% were in clinical phases. Most participants (80.7%) reported gaining knowledge about AI primarily through media sources. Radiology, general surgery, and diagnostic decision-making were identified as the disciplines most influenced by AI. According to MAIRS-MS results, students demonstrated the highest readiness in ethical aspects and the lowest in cognitive domains. The mean overall readiness score was 63.19±12.86 (out of 100). Participants with previous AI-related education achieved significantly higher readiness scores compared with those without such training (β = 6.36, p < 0.001).
Conclusion: Iranian medical students showed substantial interest in AI, although their levels of readiness varied, with stronger ethical understanding and weaker cognitive competencies. Exposure to prior AI education was linked to higher readiness scores, emphasizing the necessity of incorporating structured AI instruction into medical curricula. Overall, these results underscore the importance of targeted curriculum development to equip future healthcare professionals with the skills required for effective AI integration.

تازه های تحقیق

Pooria Sobhanian (Google Scholar)

Sara Eslami (Google Scholar)

کلیدواژه‌ها
Artificial Intelligence؛ Students؛ Medical؛ Attitude؛ Readiness؛ Cross-Sectional Studies
سایر فایل های مرتبط با مقاله
مراجع
  1. Wang F, Preininger A. AI in Health: State of the Art, Challenges, and Future Directions. Yearb Med Inform. 2019;28(01):016-26. doi: 10.1055/s-0039-1677908. PubMed PMID: 31419814 ; PubMed Central PMCID: PMC6697503.
  2. Stewart J, Lu J, Gahungu N, Goudie A, Fegan PG, Bennamoun M, Sprivulis P, Dwivedi G. Western Australian Medical Students' Attitudes Towards Artificial Intelligence in Healthcare. PLoS One. 2023;18(8):e0290642. doi: 10.1371/journal.pone.0290642. PubMed PMID: 37651380; PubMed Central PMCID: PMC10470885.
  3. Topol EJ. High-Performance Medicine: the Convergence of Human and Artificial Intelligence. Nat Med. 2019;25(1):44-56. doi: 10.1038/s41591-018-0300-7 ; PubMed PMID: 30617339.
  4. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. doi: 10.1136/svn-2017-000101. PubMed PMID: 29507784; PubMed Central PMCID: PMC5829945.
  5. Xiang Y, Zhao L, Liu Z, Wu X, Chen J, Long E, Lin D, Zhu Y, Chen C, Lin Z, Lin H. Implementation of artificial intelligence in medicine: Status analysis and development suggestions. Artif Intell Med. 2020;102:101780. doi: 10.1016/j.artmed.2019.101780. PubMed PMID: 31980086.
  6. Truong NM, Vo TQ, Tran HTB, Nguyen HT, Pham VNH. Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam. Heliyon. 2023;9(12):e22653. doi: 10.1016/j.heliyon.2023.e22653. PubMed PMID: 38107295; PubMed Central PMCID: PMC10724669.
  7. Khumrin P, Ryan A, Judd T, Verspoor K. Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students. Stud Health Technol Inform. 2017;245:447-451. PubMed PMID: 29295134.
  8. Mousavi Baigi SF, Sarbaz M, Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Kimiafar K. ttitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Sci Rep. 2023;6(3):e1138. doi: 10.1002/hsr2.1138. PubMed PMID:36923372 ; PubMed Central PMCID:PMC10009305.
  9. Moodi Ghalibaf A, Moghadasin M, Emadzadeh A, Mastour H. Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). BMC Med Educ. 2023;23(1):577. doi: 10.1186/s12909-023-04553-1. PubMed PMID: 37582816; PubMed Central PMCID: PMC10428571.
  10. Rezazadeh H, Ahmadipour H, Salajegheh M. Psychometric Evaluation of Persian Version of Medical Artificial Intelligence Readiness Scale for Medical Students. BMC Med Educ. 2023;23(1):527. doi: 10.1186/s12909-023-04516-6. PubMed PMID: 37488522 ; PubMed Central PMCID: PMC10367280.
  11. Hamd Z, Elshami W, Al Kawas S, Aljuaid H, Abuzaid MM. A closer look at the current knowledge and prospects of artificial intelligence integration in dentistry practice: A cross-sectional study. Heliyon. 2023 Jun 8;9(6):e17089. doi: 10.1016/j.heliyon.2023.e17089. PubMed PMID: 37332919; PubMed Central PMCID: PMC10276225.
  12. Doumat G, Daher D, Ghanem N-N, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: A national survey study. Front Artif Intell. 2022;5:1015418. doi:10.3389/frai.2022.1015418. PubMed PMID: 36406470 ; PubMed Central PMCID: PMC9668059.
  13. Tung AYZ, Dong LW. Malaysian medical students’ attitudes and readiness toward AI (artificial intelligence): a cross-sectional study. J Med Educ Curric Dev. 2023;10:23821205231201164 doi:10.1177/23821205231201164. PubMed PMID: 37719325 ; PubMed Central PMCID:PMC10501060.
  14. Liu DS, Sawyer J, Luna A, Aoun J, Wang J, Boachie L, Halabi S, Joe B. Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study. JMIR Med Educ. 2022;8(4):e38325. doi: 10.2196/38325. PubMed PMID: 36269641; PubMed Central PMCID: PMC9636531.
  15. Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS)–development, validity and reliability study. BMC Med Educ. 2021;21:1-9 doi:10.1186/s12909-021-02546-6. PubMed PMID:33602196 ; PubMed Central PMCID:PMC7890640.
  16. Hamd ZY, Almohammed HI, Lashin MM, Yousef M, Aljuaid H, Khawaji SM, et al. Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population. Radiat Phys Chem. 2023;208:110888. doi:10.1016/j.radphyschem.2023.110888.
  17. Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Soc Sci Med. 2022;296:114782. doi:10.1016/j.socscimed.2022.114782. PubMed PMID:35152047.
  18. Bancoro JCM. The Relationship Between Artificial Intelligence (AI) Usage and Academic Performance of Business Administration Students. International Journal of Asian Business and Management. 2024;3(1):27-48. doi: 10.55927/ijabm.v3i1.7876.
  19. Igbokwe IC. Application of Artificial Intelligence (AI) in Educational Management. IJSRP. 2023;13(3):300-307. doi:10.29322/IJSRP.13.03.2023.p13536.
  20. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage Sci. 2000;46(2):186-204. doi:10.1287/mnsc.46.2.186.11926.
  21. Lu Y. Artificial intelligence: a survey on evolution, models, applications and future trends. JMA. 2019;6(1):1-29. doi: 10.1080/23270012.2019.1570365.
  22. Na S, Heo S, Han S, Shin Y, Roh Y. Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings. 2022;12(2):90. doi:10.3390/buildings12020090.
  23. Stewart J, Freeman S, Eroglu E, Dumitrascu N, Lu J, Goudie A, et al. Attitudes towards artificial intelligence in emergency medicine. Emerg Med Australas. 2024;36(2):252-65. doi:10.1111/1742-6723.14345. PubMed PMID:38044755.
  24. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):689. doi: 10.1186/s12909-023-04698-z. PubMed PMID: 37740191; PubMed Central PMCID: PMC10517477.
  25. Ghaddaripouri K, Mousavi Baigi SF, Abbaszadeh A, Mazaheri Habibi MR. Attitude, awareness, and knowledge of telemedicine among medical students: A systematic review of cross‐sectional studies. Health Sci Rep. 2023;6(3):e1156. doi:10.1002/hsr2.1156. PubMed PMID: 36992712; PubMed Central PMCID: PMC10042283.
  26. Lomis K, Jeffries P, Palatta A, Sage M, Sheikh J, Sheperis C, Whelan A. Artificial Intelligence for Health Professions Educators. NAM Perspect. 2021;2021:10.31478/202109a. doi: 10.31478/202109a. PubMed PMID: 34901780; PubMed Central PMCID: PMC8654471.
  27. Minh D, Wang HX, Li YF, Nguyen TN. Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review. 2022;55(5):3503-68. doi: 10.1007/s10462-021-10088-y.
  28. Dehnavieh R, Inayatullah S, Yousefi F, Nadali M. Artificial Intelligence (AI) and the future of Iran’s Primary Health Care (PHC) system. BMC Prim Care. 2025;26(1):75. doi: 10.1186/s12875-025-02773-6. PubMed PMID:40119271; PubMed Central PMCID: PMC11927332.
  29. Grunhut J, Marques O, Wyatt AT. Needs, challenges, and applications of artificial intelligence in medical education curriculum. JMIR Med Educ. 2022;8(2):e35587. doi:10.2196/35587. PubMed PMID: 35671077; PubMed Central PMCID: PMC9214616.
  30. Da Silveira Marques LGV. AI-Assisted Tasks for Digital Transformation: Ethical Considerations, Impact and Implications for the Future of Work. [Dissertation]. Lisbon: NOVA Information Management School; 2024.
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