Challenges and barriers to integrating artificial intelligence in medical education and healthcare: a qualitative exploration
5 affiliations
- 1Department of Medical-Surgical Nursing, School of Nursing, Fasa University of Medical Sciences, Fasa, Iran
- 2Student Research Committee, School of Nursing, Fasa University of Medical Sciences, Fasa, Iran
- 3Department of English Language, Fasa University of Sciences, Fasa, Iran Medical
- 4Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
- 5University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Abstract
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.
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BibTeX
@article{abadi2026challenges,
title = {{Challenges and barriers to integrating artificial intelligence in medical education and healthcare: a qualitative exploration}},
author = {Fozieh Abadi and Erfan Pourshahri and Mostafa Bijani and Faezeh Abadi and Amir Faghihi and Ali Taghinezhad and Ali Abadi and Zahra Khiyali},
journal = {Journal of Medical Education Development},
year = {2026},
month = aug,
doi = {10.66224/edcj.19.3.30},
url = {https://doi.org/10.66224/edcj.19.3.30},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Challenges and barriers to integrating artificial intelligence in medical education and healthcare: a qualitative exploration
AU - Fozieh Abadi
AU - Erfan Pourshahri
AU - Mostafa Bijani
AU - Faezeh Abadi
AU - Amir Faghihi
AU - Ali Taghinezhad
AU - Ali Abadi
AU - Zahra Khiyali
PY - 2026
DA - 2026/08/01
JO - Journal of Medical Education Development
DO - 10.66224/edcj.19.3.30
UR - https://doi.org/10.66224/edcj.19.3.30
AB - 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.
SN - 2251-9521
SN - 2980-7670
ER -