Research Post

Problems associated with the deployment of machine learning-based models in health


In a companion article, Verma and colleagues discuss how machine-learned solutions can be developed and implemented to support medical decision-making.1 Both decision-support systems and clinical prediction tools developed using machine learning (including the special case of deep learning) are similar to clinical support tools developed using classical statistical models and, as such, have similar limitations.2,3 A model that makes incorrect predictions can lead its users to make errors they otherwise would not have made when caring for patients, and therefore it is important to understand how these models can fail.4 We discuss these limitations — focusing on 2 issues in particular: out-of-distribution (or out-of-sample) generalization and incorrect feature attribution — to underscore the need to consider potential caveats when using machine-learned solutions.

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