Babic, Boris and Gerke, Sara and Evgeniou, Theodoros and Cohen, Glenn
(2019)
Algorithms on Regulatory Lockdown in Medicine.
Science.
Abstract
Regulators of medical Artificial Intelligence and Machine Learning (AI/ML) are faced with a difficult problem: Should they limit marketing to a version of the system that was submitted for initial premarket review (a “locked” regime), or permit marketing of an algorithm that can adapt to changing conditions (an “adaptive” regime)? In April 2019 FDA issued a draft framework to address this problem that may become a model worldwide. In this essay, we argue that the locked/adaptive distinction and FDA’s proposed approach to it miss the central risks of medical AI/ML. Such risks emerge from structural features of predictive analytics, like concept drift, covariate shift, and AI/ML model instability. Paying attention to these risks suggests a continuous and cooperative monitoring framework of how the systems work in different and likely evolving environments, which we outline by way of conclusion.
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