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The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

Gerke, Sara and Babic, Boris and Evgeniou, Theodoros and Cohen, Glenn (2020) The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. Nature Digital Medicine.

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Abstract

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Gerke, Sara
Babic, Boris0000000328001307
Evgeniou, Theodoros
Cohen, Glenn
Subjects: Specific Sciences > Artificial Intelligence > AI and Ethics
Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User: Boris Babic
Date Deposited: 15 Mar 2021 04:21
Last Modified: 15 Mar 2021 04:21
Item ID: 18795
Journal or Publication Title: Nature Digital Medicine
Subjects: Specific Sciences > Artificial Intelligence > AI and Ethics
Specific Sciences > Artificial Intelligence > Machine Learning
Date: 2020
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/18795

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