Sat, Jun 6, 2026
[Archive]
Fozieh Abadi1 
, Erfan Pourshahri2 , Mostafa Bijani *3 , Faezeh Abadi1 , Amir Faghihi1 , Ali Taghinezhad4 , Ali Abadi5 , Zahra Khiyali6
, Erfan Pourshahri2 , Mostafa Bijani *3 , Faezeh Abadi1 , Amir Faghihi1 , Ali Taghinezhad4 , Ali Abadi5 , Zahra Khiyali6
1- Department of Medical-Surgical Nursing, School of Nursing, Fasa University of Medical Sciences, Fasa, Iran
2- Student Research Committee, School of Nursing, Fasa University of Medical Sciences, Fasa, Iran
3- Department of Medical-Surgical Nursing, School of Nursing, Fasa University of Medical Sciences, Fasa, Iran ,bizhani_mostafa@yahoo.com
4- Department of English Language, Fasa University of Sciences, Fasa, Iran Medical
5- Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
6- University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
2- Student Research Committee, School of Nursing, Fasa University of Medical Sciences, Fasa, Iran
3- Department of Medical-Surgical Nursing, School of Nursing, Fasa University of Medical Sciences, Fasa, Iran ,
4- Department of English Language, Fasa University of Sciences, Fasa, Iran Medical
5- Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
6- University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Abstract: (46 Views)
Background & Objective: Artificial Intelligence (AI) is reshaping healthcare delivery and medical education. Understanding faculty experiences with the challenges of AI implementation is essential for developing effective solutions. This study therefore aims to identify barriers to AI utilization from the perspective of medical sciences university faculty.
Materials & Methods: This qualitative study employed conventional content analysis, conducted from November 17, 2024, to January 25, 2025, in Fars Province, southern Iran. Participants were selected through purposeful sampling. Semi-structured, in-depth individual interviews were carried out with 21 faculty members who held either hands-on experience with AI or theoretical knowledge of its applications in educational and clinical settings. Data analysis followed the framework proposed by Graneheim and Lundman.
Results: Three main themes emerged. The first, individual challenges, encompassed limited knowledge and skills alongside negative attitudes and resistance toward AI. The second, organizational challenges, was reflected in inadequate facilities and infrastructure, further compounded by the absence of clear, comprehensive guidelines for AI implementation. The third, ethical challenges, surfaced concerns spanning both medical education and research.
Conclusion: The findings underscore that fully realizing AI’s transformative potential in medical sciences demands the simultaneous and coordinated strengthening of individual competencies, institutional capacities, and ethical governance. Only through an integrated, systems-oriented approach can existing barriers be effectively dismantled and broader institutional acceptance of AI within healthcare settings meaningfully advanced.
Materials & Methods: This qualitative study employed conventional content analysis, conducted from November 17, 2024, to January 25, 2025, in Fars Province, southern Iran. Participants were selected through purposeful sampling. Semi-structured, in-depth individual interviews were carried out with 21 faculty members who held either hands-on experience with AI or theoretical knowledge of its applications in educational and clinical settings. Data analysis followed the framework proposed by Graneheim and Lundman.
Results: Three main themes emerged. The first, individual challenges, encompassed limited knowledge and skills alongside negative attitudes and resistance toward AI. The second, organizational challenges, was reflected in inadequate facilities and infrastructure, further compounded by the absence of clear, comprehensive guidelines for AI implementation. The third, ethical challenges, surfaced concerns spanning both medical education and research.
Conclusion: The findings underscore that fully realizing AI’s transformative potential in medical sciences demands the simultaneous and coordinated strengthening of individual competencies, institutional capacities, and ethical governance. Only through an integrated, systems-oriented approach can existing barriers be effectively dismantled and broader institutional acceptance of AI within healthcare settings meaningfully advanced.
Keywords: artificial intelligence, medical education, qualitative research, medical ethics, barriers to adoption, digital transformation
Article Type : Orginal Research |
Subject:
Medical Education
Received: 2025/10/17 | Accepted: 2026/05/18
Received: 2025/10/17 | Accepted: 2026/05/18
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
