Regulatory responses to medical machine learning
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Authors
Minssen, Timo
Gerke, Sara
Aboy, Mateo
Price, Nicholson
Cohen, Glenn
Journal Title
Journal of Law and the Biosciences
ISSN
2053-9711
Publisher
Oxford University Press (OUP)
Language
en
Type
Article
This Version
VoR
Metadata
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Minssen, T., Gerke, S., Aboy, M., Price, N., & Cohen, G. (2020). Regulatory responses to medical machine learning. Journal of Law and the Biosciences https://doi.org/10.1093/jlb/lsaa002
Abstract
Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence, including the artificial intelligence sub-type of medical machine learning (MML). MML is based on the application of machine learning (ML) algorithms to automatically identify patterns and act on medical data to guide clinical decisions. MML poses challenges and raises important questions, including (1) How will regulators evaluate MML-based medical devices to ensure their safety and effectiveness? and (2) What additional MML considerations should be taken into account in the international context? To address these questions, we analyze the current regulatory approaches to MML in the USA and Europe. We then examine international perspectives and broader implications, discussing considerations such as data privacy, exportation, explanation, training set bias, contextual bias, and trade secrecy.
Identifiers
External DOI: https://doi.org/10.1093/jlb/lsaa002
This record's URL: https://www.repository.cam.ac.uk/handle/1810/314835
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/