Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice
Abstract
Introduction The integration of AI in medical imaging has tremendous exponential growth, especially in image production, image processing and image interpretation. It is expected that radiographers working across all imaging modalities have adequate knowledge as they are part of the end-user team. The current study aimed to investigate the knowledge, willingness and challenges facing the Magnetic Resonance Imaging (MRI) technologists in the integration of Artificial Intelligence (AI) into MRI practice.Methods Total of 120 participants were recruited using a snowball sampling technique. A two-phase study was undertaken using survey and focus group discussion (FGD) to capture participants' knowledge, interpretations, needs and obstacles toward AI integrations in MRI practice. The survey and FGD provided the base to understand the participant's' knowledge, acceptance and needs for AI.Results Results showed medium to high knowledge, excitement about AI integration without disturbance of MRI practice. Participants thought that AI can improve MRI protocol selection (91.8%), reduce the scan time (65.3%), and improve image post-processing (79.5%). Education and learning resources concerning AI were the main obstacles facing MRI technologists.Conclusion MRI technologists have the knowledge and possess basic technical information. The application of AI in MRI practice might greatly influence and improve MRI technologist's work. A structured and professional program should be integrated in both undergraduate and continuous education to prepare for effective AI implementation.Implications for practice Application of AI in MRI can be used in many aspects, such as optimize image quality and avoidance of image artifacts. Moreover, AI can play an important role in patient's safety at the MRI unit to reduce incidents. Education, infrastructure, and knowledge of end-users are keys for the incorporation of AI use, development and optimisation.
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- Authors
- Abuzaid, M. M., Tekin, H. O., Reza, M., Elhag, I. R., & Elshami, W.
- Affiliations
- University of Sharjah (AE)
- Sheikh Shakhbout Medical City (AE)
- Corresponding author
- Elshami, W.
- Venue
- Radiography
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 27
- Pages
- S83-S87
- ISSN
- 1078-8174 (ISSN-L)
- OpenAlex
- W3187241719
- Cited by
- 36
- References
- 19
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 12 · 2023: 5 · 2022: 10 · 2021: 1
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Radiography (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, radiographers, technology acceptance, AI knowledge, AI adoption barriers, AI education and training, image post-processing, continuing professional development
- MeSH terms
- Allied Health Personnel, Artificial Intelligence, Humans, Magnetic Resonance Imaging, Radiography, Radiology
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