人工智能辅助的肋骨骨折解释培训:在医学本科生中进行的一项前性研究
Yu-San Tee1,2, Chien-An Liao1,2,3, Ling-Wei Kuo1,2
1Department of Trauma and Emergency Surgery, Department of Surgery, Chang Gung Memorial Hospital, Linkou, Taoyuan City, Taiwan.
World journal of emergency surgery : WJES
|February 14, 2026
概括
人工智能 (AI) 培训显著提高了医学学生在胸部X射线 (CXR) 上检测肋骨骨折的能力. 虽然人工智能退出后性能下降,但技能仍然增强,这表明人工智能可以加强放射学教育.
科学领域:
- 医疗成像医学成像
- 放射学教育 放射学教育
- 人工智能在医学中的应用
背景情况:
- 胸部X射线 (CXR) 在创伤护理中至关重要,但在检测肋骨骨折方面存在局限性,往往导致错过诊断.
- 人工智能 (AI) 在增强放射学教育中的作用,特别是对解读创伤CXR的作用,尚未得到充分证实.
研究的目的:
- 评估人工智能辅助培训对医学学生诊断表现和识别伤口CXR上的肋骨骨折的信心的影响.
- 在移除人工智能辅助后评估技能保留.
主要方法:
- 一项前性观察性研究,涉及26名医学本科生.
- 学生进行了三次解读:基线无助,人工智能辅助和后人工智能无助解读创伤CXRs.
- 诊断性绩效指标和信心水平在不同会议之间进行了比较.
主要成果:
- 人工智能辅助显著提高了准确性,灵敏度,特异性,F1得分和精度 (所有p < 0.01).
- 与基线相比,AI后的解释显示了准确性和精度的持续改善 (p=0.010).
- 在整个培训过程中,信心水平持续上升 (p < 0.001),观察到潜在的自动化偏差.
结论:
- 人工智能辅助培训显著改善了早期诊断性能和在CXR上检测肋骨骨折的信心.
- 在AI退出后,部分技能保留发生,这表明AI有可能加强放射学培训.
- 需要策略来缓解自动化偏见,并在AI集成培训计划中促进独立的诊断判断.
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