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Self-supervised learning in medicine and healthcare


The development of medical applications of machine learning has required manual annotation of data, often by medical experts. Yet, the availability of large-scale unannotated data provides opportunities for the development of better machine-learning models. In this Review, we highlight self-supervised methods and models for use in medicine and healthcare, and discuss the advantages and limitations of their application to tasks involving electronic health records and datasets of medical images, bioelectrical signals, and sequences and structures of genes and proteins. We also discuss promising applications of self-supervised learning for the development of models leveraging multimodal datasets, and the challenges in collecting unbiased data for their training. Self-supervised learning may accelerate the development of medical artificial intelligence.

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Fig. 1: Contrastive learning.
Fig. 2: Generative pre-training.


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We thank A. Saporta and A. Tamkin for their helpful suggestions. We acknowledge support from the NIH grant UL1TR002550 to E.J.T.

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Correspondence to Pranav Rajpurkar.

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Nature Biomedical Engineering thanks Su-In Lee, Faisal Mahmood and Collin Stultz for their contribution to the peer review of this work.

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Krishnan, R., Rajpurkar, P. & Topol, E.J. Self-supervised learning in medicine and healthcare. Nat. Biomed. Eng 6, 1346–1352 (2022).

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