Second opinion needed: communicating uncertainty in medical machine learning.
Second opinion needed: communicating uncertainty in medical machine learning.
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DOI：
10.1038/s41746-020-00367-3
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发表时间：
2021-01-05
影响因子：
15.2
通讯作者：
Beam AL
中科院分区：
文献类型：
--
作者：
Kompa B;Snoek J;Beam AL
令人兴奋的是，基于机器学习（ML）的医疗人工智能（AI）可用于改善各种医疗保健环境中患者层面的决策。然而，个体预测的不确定性的量化和传达往往被忽视，即使不确定性估计可能导致更有原则的决策，并使机器学习模型能够自动或半自动地放弃具有高不确定性的样本。在本文中，我们概述了机器学习的不确定性量化和消除的不同方法，并强调了这些技术如何提高当前在医疗保健环境中使用的ML系统的安全性和可靠性。对不确定性的有效量化和沟通可以帮助医疗工作者建立信任，同时为当前机器学习方法的已知故障模式提供保障。随着机器学习进一步集成到医疗环境中，在不确定时说“我不确定”或“我不知道”的能力是实现安全临床部署的必要能力。
There is great excitement that medical artificial intelligence (AI) based on machine learning (ML) can be used to improve decision making at the patient level in a variety of healthcare settings. However, the quantification and communication of uncertainty for individual predictions is often neglected even though uncertainty estimates could lead to more principled decision-making and enable machine learning models to automatically or semi-automatically abstain on samples for which there is high uncertainty. In this article, we provide an overview of different approaches to uncertainty quantification and abstention for machine learning and highlight how these techniques could improve the safety and reliability of current ML systems being used in healthcare settings. Effective quantification and communication of uncertainty could help to engender trust with healthcare workers, while providing safeguards against known failure modes of current machine learning approaches. As machine learning becomes further integrated into healthcare environments, the ability to say “I’m not sure” or “I don’t know” when uncertain is a necessary capability to enable safe clinical deployment.
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影响因子：
2.5
作者：
CHOW, CK
通讯作者：
CHOW, CK
DOI：
10.1126/science.aaw4399
发表时间：
2019-03-22
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Finlayson SG;Bowers JD;Ito J;Zittrain JL;Beam AL;Kohane IS
通讯作者：
Kohane IS
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
5.7
作者：
Breiman, L
通讯作者：
Breiman, L
DOI：
10.1007/3-540-45014-9_1
发表时间：
2000-01-01
期刊：
MULTIPLE CLASSIFIER SYSTEMS
影响因子：
--
作者：
Dietterich, TG
通讯作者：
Dietterich, TG