Li Ruili, Liu Guangxue, Zhang Miao, Rong Dongdong, Su Zhuangzhi, Shan Yi, Lu Jie
Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Department of Natural Medicines, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, 100191, China.
BMC Med Educ. 2025 Mar 13;25(1):380. doi: 10.1186/s12909-025-06859-8.
To investigate the perspectives and expectations of faculty radiologists, residents, and medical students regarding the integration of artificial intelligence (AI) in radiology education, a survey was conducted to collect their opinions and attitudes on implementing AI in radiology education.
An online questionnaire was used for this survey, and the participant anonymity was ensured. In total, 41 faculty radiologists, 38 residents, and 120 medical students from the authors' institution completed the questionnaire.
Most residents and students experience different levels of psychological stress during the initial stage of clinical practice, and this stress mainly stems from tight schedules, heavy workloads, apprehensions about making mistakes in diagnostic report writing, as well as academic or employment pressures. Although most of the respondents were not familiar with how AI is applied in radiology education, a substantial proportion of them expressed eagerness and enthusiasm for the integration of AI into radiology education. Especially among radiologists and residents, they showed a desire to utilize an AI-driven online platform for practicing radiology skills, including reading medical images and writing diagnostic reports, before engaging in clinical practice. Furthermore, faculty radiologists demonstrated strong enthusiasm for the notion that AI training platforms can enhance training efficiency and boost learners' confidence. Notably, only approximately half of the residents and medical students shared the instructors' optimism, with the remainder expressing neutrality or concern, emphasizing the need for robust AI feedback systems and user-centered designs. Moreover, the authors' team has developed a preliminary framework for an AI-driven radiology education training platform, consisting of four key components: imaging case sets, intelligent interactive learning, self-quiz, and online exam.
The integration of AI technology in radiology education has the potential to revolutionize the field by providing innovative solutions for enhancing competency levels and optimizing learning outcomes.
为了调查放射科医生、住院医师和医学生对于人工智能（AI）融入放射学教育的观点和期望，开展了一项调查以收集他们对于在放射学教育中实施AI的意见和态度。
本次调查采用在线问卷，并确保参与者匿名。来自作者所在机构的41名放射科医生、38名住院医师和120名医学生完成了问卷。
大多数住院医师和学生在临床实践初期经历不同程度的心理压力，这种压力主要源于日程安排紧张、工作量大、对诊断报告撰写中犯错的担忧以及学术或就业压力。尽管大多数受访者不熟悉AI在放射学教育中的应用方式，但他们中的很大一部分人对AI融入放射学教育表示热切和热情。尤其是放射科医生和住院医师，他们表示希望在临床实践之前利用AI驱动的在线平台来练习放射学技能，包括阅读医学影像和撰写诊断报告。此外，放射科医生对AI培训平台可以提高培训效率和增强学习者信心的观点表现出强烈热情。值得注意的是，只有大约一半的住院医师和医学生与教师持乐观态度，其余人则表示中立或担忧，强调需要强大的AI反馈系统和以用户为中心的设计。此外，作者团队已经开发了一个AI驱动的放射学教育培训平台的初步框架，由四个关键组件组成：影像病例集、智能交互式学习、自我测验和在线考试。
AI技术融入放射学教育有可能通过提供创新解决方案来提高能力水平和优化学习成果，从而彻底改变该领域。
