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Published on: July 11, 2025
Urgent Need for Artificial Intelligence Readiness: Insights From a Multicenter Cross-Sectional Study on Medical
Hui Qiu1, Guojin Zhang1, JunFeng Li2
1Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China (H.Q., G.Z., Y.L., L.S.).
Rationale And Objectives:
Artificial intelligence (AI) is reshaping the future of medicine, particularly influencing specialties like radiology. While the adoption of AI continues to accelerate, its integration presents both opportunities and challenges. To date, limited research has examined the perspectives of medical students and radiology trainees in less-developed regions of China-groups essential to the future healthcare workforce. This study aimed to assess their perceptions, attitudes, usage, and concerns regarding AI in clinical practice.
Materials And Methods:
A cross-sectional, multicenter study was conducted between February and March 2025 across three provinces in central and western China. A total of 5043 medical undergraduates and 190 radiology trainees completed a self-designed questionnaire assessing their knowledge of AI, perceived utility, and awareness of its clinical applications.
Results:
Most medical undergraduates reported limited exposure to formal AI training or hands-on experience yet expressed general support for its clinical use. Concerns regarding data privacy, transparency, and patient trust were commonly noted. Radiology trainees demonstrated higher levels of AI education and tool utilization. Both groups agreed AI could enhance efficiency without replacing physicians and emphasized the need to address technical, legal, and ethical challenges for successful implementation. Further analysis presented both grades and gender will influence participants' attitudes toward AI.
Conclusion:
This study highlights a generally positive attitude toward AI among future healthcare professionals, while revealing substantial educational gaps. Structured AI training should be integrated into undergraduate and radiology curricula to better prepare trainees for AI-assisted clinical environments.