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Regulatory responses to medical machine learning

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Standard

Regulatory responses to medical machine learning. / Minssen, Timo; Gerke, Sara; Aboy, Mateo; Price II, William Nicholson; Cohen, Glenn .

I: Journal of Law and the Biosciences, Bind 7, Nr. 1, 04.2020, s. 1-18.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Minssen, T, Gerke, S, Aboy, M, Price II, WN & Cohen, G 2020, 'Regulatory responses to medical machine learning', Journal of Law and the Biosciences, bind 7, nr. 1, s. 1-18. https://doi.org/10.1093/jlb/lsaa002

APA

Minssen, T., Gerke, S., Aboy, M., Price II, W. N., & Cohen, G. (2020). Regulatory responses to medical machine learning. Journal of Law and the Biosciences, 7(1), 1-18. https://doi.org/10.1093/jlb/lsaa002

Vancouver

Minssen T, Gerke S, Aboy M, Price II WN, Cohen G. Regulatory responses to medical machine learning. Journal of Law and the Biosciences. 2020 apr;7(1):1-18. https://doi.org/10.1093/jlb/lsaa002

Author

Minssen, Timo ; Gerke, Sara ; Aboy, Mateo ; Price II, William Nicholson ; Cohen, Glenn . / Regulatory responses to medical machine learning. I: Journal of Law and the Biosciences. 2020 ; Bind 7, Nr. 1. s. 1-18.

Bibtex

@article{960452b282234b64b2b3334bbeee58c7,
title = "Regulatory responses to medical machine learning",
abstract = "Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence (MAI), including the AI 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 United States 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.",
author = "Timo Minssen and Sara Gerke and Mateo Aboy and {Price II}, {William Nicholson} and Glenn Cohen",
year = "2020",
month = "4",
doi = "https://doi.org/10.1093/jlb/lsaa002",
language = "English",
volume = "7",
pages = "1--18",
journal = "Journal of Law and the Biosciences",
issn = "2053-9711",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - Regulatory responses to medical machine learning

AU - Minssen, Timo

AU - Gerke, Sara

AU - Aboy, Mateo

AU - Price II, William Nicholson

AU - Cohen, Glenn

PY - 2020/4

Y1 - 2020/4

N2 - Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence (MAI), including the AI 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 United States 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.

AB - Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence (MAI), including the AI 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 United States 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.

UR - https://academic.oup.com/jlb/article/doi/10.1093/jlb/lsaa002/5817484?searchresult=1

U2 - https://doi.org/10.1093/jlb/lsaa002

DO - https://doi.org/10.1093/jlb/lsaa002

M3 - Journal article

VL - 7

SP - 1

EP - 18

JO - Journal of Law and the Biosciences

JF - Journal of Law and the Biosciences

SN - 2053-9711

IS - 1

ER -

ID: 215933720