Regulatory Responses to Medical Machine Learning
Author ORCID iD
https://orcid.org/0000-0002-5718-3982
Document Type
Article
Publication Date
7-25-2020
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.
Publication Title
Journal of Law and the Biosciences
Recommended Citation
Timo Minssen, Sara Gerke, Mateo Aboy, Nicholson Price, and Glenn Cohen, Regulatory Responses to Medical Machine Learning, 7 Journal of Law and the Biosciences 1 (2020).
Comments
"This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed."