Chen Yanqiu, Sun Zhen, Lin Wenjie, Xv Ziwei, Su Qichen
Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Zhongshan North Road 34#, Quanzhou, 362000, China.
Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, China.
J Cancer Educ. 2025 Apr;40(2):234-240. doi: 10.1007/s13187-024-02502-0. Epub 2024 Sep 7.
The aim of the present study was to compare the effectiveness of AI-assisted training and conventional human training in clinical practice. This was a multicenter, randomized, controlled clinical trial conducted in five national-level residency training hospitals. Residents from five hospitals participated, divided into three groups: conventional training (Group A), conventional plus specialty training (Group B), and conventional plus AI-assisted training (Group C). The content of the training was ultrasound diagnosis of thyroid nodules. The training lasted for 18 months, and the three groups of participants were phase-tested every 3 months to compare the effect of the training. The diagnostic accuracy of all three groups gradually increased with increasing training time. Among the three groups, groups B and C had higher accuracy than group A (P < .001), and there was no significant difference between groups B and C (P = .64). Over the training period, diagnostic confidence increased in all groups. Negative activating emotions decreased significantly over time in all groups (95% CI, - 0.81 to - 0.37; P < .001), while positive activating emotions increased significantly (95% CI, 0.18 to 0.53; P < .001). Current research shows that all three approaches are viable for training radiology residents. Furthermore, the AI-assisted approach had no negative emotional impact on the trainees, suggesting that integrating AI into radiology training programs could provide a reliable and effective means of achieving the educational goals of medical education.
本研究的目的是比较人工智能辅助培训与传统人工培训在临床实践中的有效性。这是一项在五家国家级住院医师培训医院进行的多中心、随机对照临床试验。来自五家医院的住院医师参与其中，分为三组：传统培训组（A组）、传统加专业培训组（B组）和传统加人工智能辅助培训组（C组）。培训内容为甲状腺结节的超声诊断。培训持续18个月，三组参与者每3个月进行一次阶段性测试，以比较培训效果。三组的诊断准确性均随着培训时间的增加而逐渐提高。在三组中，B组和C组的准确性高于A组（P < .001），B组和C组之间无显著差异（P = .64）。在培训期间，所有组的诊断信心均有所提高。所有组中，消极激活情绪随时间显著下降（95%CI，-0.81至-0.37；P < .001），而积极激活情绪显著增加（95%CI，0.18至0.53；P < .001）。当前研究表明，所有三种方法对于培训放射科住院医师都是可行的。此外，人工智能辅助方法对受训者没有负面情绪影响，这表明将人工智能融入放射科培训项目可以提供一种可靠且有效的手段来实现医学教育的教学目标。
