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Multimodal deep learning has been used to predict clinical endpoints anddiagnoses from clinical routine data. However, these models suffer from scalingissues: they have to learn pairwise interactions between each piece ofinformation in each data type, thereby escalating model complexity beyondmanageable scales. This has so far precluded a widespread use of multimodaldeep learning. Here, we present a new technical approach of "learnablesynergies", in which the model only selects relevant interactions between datamodalities and keeps an "internal memory" of relevant data. Our approach iseasily scalable and naturally adapts to multimodal data inputs from clinicalroutine. We demonstrate this approach on three large multimodal datasets fromradiology and ophthalmology and show that it outperforms state-of-the-artmodels in clinically relevant diagnosis tasks. Our new approach is transferableand will allow the application of multimodal deep learning to a broad set ofclinically relevant problems.

Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis