Lessons from shortcomings in machine learning for medical imaging

Abstract

The application of machine learning (ML) to medical imaging has attracted a lot of attention in recent years. Yet, for various reasons, progress remains slow. This essay builds upon earlier work by the authors which explores how larger datasets and more deep-learning algorithms have not yet provided practical improvements in addressing clinical problems. It recommends how researchers and policy makers can improve the situation.

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