从机器学习:人工智能辅助不是一个有效的学习工具,在胸部X射线解释的居民教育
Guillaume Chassagnon1,2, Nicolas Billet3, Caroline Rutten3
1Radiology Department, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France. guillaume.chassagnon@aphp.fr.
European radiology
|August 12, 2023
概括
计算机辅助检测 (CADe) 系统可以提高放射科医生的胸部X射线解释精度. 然而,单靠CADe并不是一个有效的学习工具,它应该补充,而不是取代传统的教学方法.
科学领域:
- 放射学 放射学是一门学科.
- 医学教育 医学教育
- 人工智能在医学中的应用
背景情况:
- 放射科住院医生需要在解释胸部X射线 (CXRs) 方面进行强有力的培训.
- 计算机辅助检测 (CADe) 系统在图像解释方面提供了潜在的帮助.
- 评估CADe对居民培训的教育价值至关重要.
研究的目的:
- 评估CADe系统是否可以作为放射科住院医生解读CXR的学习工具.
- 为了比较有或没有CADe辅助的住院医生的诊断性能.
- 确定CADe对居民学习的长期影响.
主要方法:
- 八名放射科医生解释了500个CXR,检测了五种常见的异常.
- 在初步培训后,居民被分为两组.
- 一组在口译过程中使用CADe作为第二个读者,而对照组没有.
- 性能指标 (灵敏度,特异性,准确性) 在CADe使用之前,期间和之后进行了比较.
主要成果:
- 与对照组相比,使用CADe的居民显著提高了灵敏度 (53%vs. 43%),特异性 (94%vs. 90%),准确性 (86%vs. 81%).
- 在删除了CADe访问后,两组的表现水平在统计上变得相似.
- 在干预后的组之间没有观察到敏感度,特异性或准确性的显著差异.
结论:
- 虽然CADe提高了居民的CXR即时解释性能,但它似乎不是一个独立的有效学习工具.
- 应将CADe系统视为补充工具,增强而不是取代专用教学和居民教育.
- 这些发现强调了在放射学培训中继续使用传统教学方法的重要性.
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