Document Type
Article
Publication Date
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.
Recommended Citation
Price, W. Nicholson, II. "Regulatory Responses to Medical Machine Learning." Timo Minssen, Sara Gerke, Mateo Aboy, and Glen Cohen, co-authors. J. L. & Biosciences 7, no. 1 (2020): 1-18.
Included in
Comparative and Foreign Law Commons, Health Law and Policy Commons, Science and Technology Law Commons
Comments
© The Author(s) 2020. Published by Oxford University Press on behalf of Duke University School of Law, Harvard Law School, Oxford University Press, and Stanford Law School.
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