Regulatory responses to medical machine learning
(2020) In Journal of Law and the Biosciences 7(1). p.1-18- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3202d68a-02c3-4714-9ed4-4b5131b7e492
- author
- Minssen, Timo LU ; Gerke, Sara ; Aboy, Mateo ; Price II, William Nicholson and Cohen, Glenn
- publishing date
- 2020-04-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Medical law, Medicinsk rätt
- in
- Journal of Law and the Biosciences
- volume
- 7
- issue
- 1
- pages
- 18 pages
- publisher
- Oxford University Press
- external identifiers
-
- scopus:85087057978
- ISSN
- 2053-9711
- DOI
- 10.1093/jlb/lsaa002
- project
- The Quantum Law Project
- language
- English
- LU publication?
- no
- id
- 3202d68a-02c3-4714-9ed4-4b5131b7e492
- date added to LUP
- 2020-12-16 13:16:32
- date last changed
- 2022-04-26 22:47:26
@article{3202d68a-02c3-4714-9ed4-4b5131b7e492,
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 = {{Minssen, Timo and Gerke, Sara and Aboy, Mateo and Price II, William Nicholson and Cohen, Glenn}},
issn = {{2053-9711}},
keywords = {{Medical law; Medicinsk rätt}},
language = {{eng}},
month = {{04}},
number = {{1}},
pages = {{1--18}},
publisher = {{Oxford University Press}},
series = {{Journal of Law and the Biosciences}},
title = {{Regulatory responses to medical machine learning}},
url = {{http://dx.doi.org/10.1093/jlb/lsaa002}},
doi = {{10.1093/jlb/lsaa002}},
volume = {{7}},
year = {{2020}},
}