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ZENODO
Other literature type . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Other literature type . 2024
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2024
License: CC BY
Data sources: Datacite
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Usability Engineering for Medical Devices using Artificial Intelligence and Machine Learning Technology

descriptionPublicationkeyboard_double_arrow_right Other literature type 22 Nov 2024 English Publisher:Zenodo
Authors: Engler, Michael; Johner, Christian; Krepcke, Martin; Martinez Torres, Manuel Isaac; Stangenberg, Martin;

doi: 10.5281/zenodo.14203190 , 10.5281/zenodo.14203189

Usability Engineering for Medical Devices using Artificial Intelligence and Machine Learning Technology

Abstract

This paper explores how to apply the established IEC 62366-1 usability engineering process for safe and effective use of Artificial Intelligence (AI) and machine learning in medical devices. We identify unique factors specific to AI technology that can impact safe use and integrate these considerations into a comprehensive analysis of IEC 62366-1. This analysis results in a step-by-step guide with practical recommendations for identifying, evaluating, and mitigating use-related risks specific to AI devices.

Related Organizations
Keywords

Machine Learning, Usability, Human Factors Engineering, Medical Device, IEC 62366-1

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green