可以解释一下吗? 人工智能解释类型不同影响胸部X射线图诊断性能和医生对人工智能的信任
Drew Prinster1, Amama Mahmood1, Suchi Saria1
1From the Department of Computer Science, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218 (D.P., A.M., S.S., C.M.H.); Bayesian Health, New York, NY (S.S.); Department of Diagnostic Radiology, University of Maryland School of Medicine, Baltimore, Md (J.J., P.H.Y.); Department of Radiology, St Jude Children's Research Hospital, Memphis, Tenn (P.H.Y.); and Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Md (C.T.L.).
Radiology
|November 19, 2024
概括
与全球解释相比,本地人工智能 (AI) 解释提高了放射科医生的诊断准确性和效率. 这种AI特征类型也增强了医生对AI建议的信任,用于胸部X射线图解读.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 人工智能 (AI) 解释对放射学医生诊断绩效的影响尚不清楚.
- 了解人工智能解释如何影响决策对于有效地将人工智能整合到医疗保健中至关重要.
研究的目的:
- 评估AI解释类型 (本地与全球) 和AI建议特征 (正确性,信心) 如何影响医生的诊断性能,感知到的有用性和信任.
- 为了在胸部X光学诊断的背景下研究这些影响.
主要方法:
- 一项多中心,前性随机研究,涉及220名医生 (放射科医生和非专家).
- 参与者通过模拟人工智能建议审查胸部X射线图,人工智能解释类型作为参与者之间的因素.
- 一般化的线性混合效应模型分析了诊断准确性,效率和信任,控制了专业知识和AI知识.
主要成果:
- 当地AI解释在AI建议正确时显著提高了诊断准确性,并提高了整体诊断效率.
- 医生任务专业知识与解释类型和AI信心相互作用,影响诊断准确性.
- 与全球解释相比,当地的解释培养了更大的"简单信任" (与人工智能建议保持一致),无论人工智能建议的正确性如何.
结论:
- 人工智能解释的类型对医生诊断性能和对人工智能系统的信任产生了重大影响.
- 在放射学决策中,基于特征的本地AI解释似乎比基于原型的全球解释更有益.
- 这些对表现和信任的影响甚至可以在没有医生意识的情况下发生.
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