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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, P< 0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (β=0.481, P< 0.001) and practice (β=0.412, P< 0.001), and attitude had a direct effect on practice (β=0.135, P< 0.001). Conclusion: Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary. [ABSTRACT FROM AUTHOR]