M M Abuzaid1, H O Tekin1, M Reza2, I R Elhag2, W Elshami3. 1. Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates. 2. Shaikh Shakeboat Medical City, Radiology Department, AbuDhabi, United Arab Emirates. 3. Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates. Electronic address: welshami@sharjah.ac.ae.
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.
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.
Authors: Xiao Chen; Qingshan Deng; Qiang Wang; Xinmiao Liu; Lei Chen; Jinjin Liu; Shuangquan Li; Meihao Wang; Guoquan Cao Journal: Front Public Health Date: 2022-04-26
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