Regulatory responses to medical machine learning (under review)

Timo Minssen; Sara Gerke; Mateo Aboy; William Nicholson Price II; Glenn  Cohen

Regulatory responses to medical machine learning (under review)

Timo Minssen, Sara Gerke, Mateo Aboy, William Nicholson Price II, Glenn Cohen

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.

Original language	English
Journal	Journal of Law and the Biosciences
Publication status	Submitted - 18 Jun 2019

Cite this

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title = "Regulatory responses to medical machine learning (under review)",

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 = "2019",

month = jun,

day = "18",

language = "English",

journal = "Journal of Law and the Biosciences ",

issn = "2053-9711",

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T1 - Regulatory responses to medical machine learning (under review)

AU - Minssen, Timo

AU - Gerke, Sara

AU - Aboy, Mateo

AU - Price II, William Nicholson

AU - Cohen, Glenn

PY - 2019/6/18

Y1 - 2019/6/18

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.

M3 - Journal article

SN - 2053-9711

JO - Journal of Law and the Biosciences

JF - Journal of Law and the Biosciences

ER -

Regulatory responses to medical machine learning (under review)

Abstract

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