How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices

• Published in: BMJ health & care informatics Vol. 28; no. 1; p. e100301
• Main Authors: Lyell, David, Coiera, Enrico, Chen, Jessica, Shah, Parina, Magrabi, Farah
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Ltd 01-04-2021; BMJ Publishing Group LTD; BMJ Publishing Group
• Subjects: Algorithms; Artificial intelligence; Automation; Clinical decision making; Clinical medicine; Decision making; Disease; FDA approval; Health informatics; Information processing; Machine learning; Mammography; Medical device industry; Medical equipment; medical informatics; Original Research; Patient safety; Software; United States > US; medical informatics
• ISSN: 2632-1009; 2632-1009
• DOI: 10.1136/bmjhci-2020-100301

ObjectiveTo examine how and to what extent medical devices using machine learning (ML) support clinician decision making.MethodsWe 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.ResultsOf 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.ConclusionLeveraging 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.