Regulatory responses to medical machine learning.
Regulatory responses to medical machine learning.
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DOI:
10.1093/jlb/lsaa002
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发表时间:
2020-01
影响因子:
3.4
通讯作者:
Cohen G
中科院分区:
文献类型:
--
作者:
Minssen T;Gerke S;Aboy M;Price N;Cohen G
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.
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影响因子:
24
作者:
Landry, Latrice G.;Rehm, Heidi L.
通讯作者:
Rehm, Heidi L.
影响因子:
17.3
作者:
Lamph, Susan
通讯作者:
Lamph, Susan
DOI:
10.1007/978-3-030-04173-1_11
发表时间:
2019-01-01
期刊:
GUIDE TO AMBIENT INTELLIGENCE IN THE IOT ENVIRONMENT: PRINCIPLES, TECHNOLOGIES AND APPLICATIONS
影响因子:
--
作者:
Ramanujam, E.;Padmavathi, S.
通讯作者:
Padmavathi, S.
DOI:
10.1056/nejmsa1507092
发表时间:
2016-08-18
期刊:
The New England journal of medicine
影响因子:
--
作者:
Manrai AK;Funke BH;Rehm HL;Olesen MS;Maron BA;Szolovits P;Margulies DM;Loscalzo J;Kohane IS
通讯作者:
Kohane IS
DOI:
10.1038/s41568-018-0016-5
发表时间:
2018-08
期刊:
Nature reviews. Cancer
影响因子:
--
作者:
Hosny A;Parmar C;Quackenbush J;Schwartz LH;Aerts HJWL
通讯作者:
Aerts HJWL