  Recent progress in Medical Artificial Intelligence (AI) has delivered systems
that can reach clinical expert level performance. However, such systems tend to
demonstrate sub-optimal "out-of-distribution" performance when evaluated in
clinical settings different from the training environment. A common mitigation
strategy is to develop separate systems for each clinical setting using
site-specific data [1]. However, this quickly becomes impractical as medical
data is time-consuming to acquire and expensive to annotate [2]. Thus, the
problem of "data-efficient generalization" presents an ongoing difficulty for
Medical AI development. Although progress in representation learning shows
promise, their benefits have not been rigorously studied, specifically for
out-of-distribution settings. To meet these challenges, we present REMEDIS, a
unified representation learning strategy to improve robustness and
data-efficiency of medical imaging AI. REMEDIS uses a generic combination of
large-scale supervised transfer learning with self-supervised learning and
requires little task-specific customization. We study a diverse range of
medical imaging tasks and simulate three realistic application scenarios using
retrospective data. REMEDIS exhibits significantly improved in-distribution
performance with up to 11.5% relative improvement in diagnostic accuracy over a
strong supervised baseline. More importantly, our strategy leads to strong
data-efficient generalization of medical imaging AI, matching strong supervised
baselines using between 1% to 33% of retraining data across tasks. These
results suggest that REMEDIS can significantly accelerate the life-cycle of
medical imaging AI development thereby presenting an important step forward for
medical imaging AI to deliver broad impact.
