Knowledge, Attitude and Practice of Radiologists Regarding Artificial Intelligence in Medical Imaging

作者：Wennuo Huang, Yuanzhe Li, Zhuqing Bao, Jing Ye, Wei Xia, Yan Lv, Jiahui Lü, Chao Wang, Xi Zhu · 发表于：Journal of Multidisciplinary Healthcare · 年份：2024 · DOI：10.2147/jmdh.s451301 · 被引用次数：11 · 研究领域：Artificial Intelligence in Healthcare and Education、Radiomics and Machine Learning in Medical Imaging、Explainable Artificial Intelligence (XAI)

Purpose: This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. Methods: This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. Results: The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, ...