How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices.
How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices.
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DOI：
10.1136/bmjhci-2020-100301
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发表时间：
2021-04
影响因子：
4.1
通讯作者：
Magrabi F
中科院分区：
文献类型：
--
作者：
Lyell D;Coiera E;Chen J;Shah P;Magrabi F
研究使用机器学习(ML)的医疗设备如何以及在多大程度上支持临床医生的决策。我们搜索到(1)经美国食品和药物管理局(FDA)批准至2020年2月的医疗器械；(2)供临床医生使用；(3)用于临床任务或决策；(4)使用ML。提取了关于临床任务、设备任务、设备输入和输出以及ML方法的描述性信息。评估了基于ML的设备自动处理人类信息的阶段和自主性水平。在137个候选设备中，包括了49个独特设备的59个FDA批准。大多数批准(n=51)是自2018年以来。设备通常协助诊断(n=35)和分诊(n=10)任务。23台设备是辅助设备，提供决策支持，但让临床医生做出包括诊断在内的重要决策。12人自动提供信息(自主信息)，如心脏射血分数的量化，而14人自动提供任务决定，如根据可疑的中风发现对扫描读数进行分类(自主决定)。设备最自动化的人类信息处理阶段是信息分析(n=14)，提供信息作为临床医生决策的输入，以及决策选择(n=29)，其中设备提供决策。要利用ML算法的优势来支持临床医生，同时降低风险，需要在临床医生和基于ML的设备之间建立牢固的关系。这种关系必须仔细设计，考虑到算法是如何嵌入设备中的、支持的任务、提供的信息以及临床医生与它们的互动。
To examine how and to what extent medical devices using machine learning (ML) support clinician decision making. We searched for medical devices that were (1) approved by the US Food and Drug Administration (FDA) up till February 2020; (2) intended for use by clinicians; (3) in clinical tasks or decisions and (4) used ML. Descriptive information about the clinical task, device task, device input and output, and ML method were extracted. The stage of human information processing automated by ML-based devices and level of autonomy were assessed. Of 137 candidates, 59 FDA approvals for 49 unique devices were included. Most approvals (n=51) were since 2018. Devices commonly assisted with diagnostic (n=35) and triage (n=10) tasks. Twenty-three devices were assistive, providing decision support but left clinicians to make important decisions including diagnosis. Twelve automated the provision of information (autonomous information), such as quantification of heart ejection fraction, while 14 automatically provided task decisions like triaging the reading of scans according to suspected findings of stroke (autonomous decisions). Stages of human information processing most automated by devices were information analysis, (n=14) providing information as an input into clinician decision making, and decision selection (n=29), where devices provide a decision. Leveraging the benefits of ML algorithms to support clinicians while mitigating risks, requires a solid relationship between clinician and ML-based devices. Such relationships must be carefully designed, considering how algorithms are embedded in devices, the tasks supported, information provided and clinicians’ interactions with them.
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影响因子：
3.3
作者：
Onnasch, Linda;Wickens, Christopher D.;Manzey, Dietrich
通讯作者：
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影响因子：
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影响因子：
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作者：
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