Self-supervised learning in medicine and healthcare
Rayan Krishnan1, Pranav Rajpurkar2, Eric J Topol3
1Department of Computer Science, Stanford University, Stanford, CA, USA.
Nature Biomedical Engineering
|August 11, 2022
Summary
Self-supervised learning offers a powerful approach to train machine learning models using large amounts of unannotated medical data. This method can accelerate the development of artificial intelligence in healthcare by overcoming manual annotation limitations.
Area of Science:
- Medical Artificial Intelligence
- Machine Learning in Healthcare
Background:
- Traditional medical machine learning relies on manually annotated data, which is time-consuming and requires expert input.
- Vast quantities of unannotated medical data are available, presenting an opportunity for advanced model development.
Purpose of the Study:
- To review self-supervised methods and models applicable to medicine and healthcare.
- To discuss the benefits and drawbacks of applying self-supervised learning to diverse medical datasets.
Main Methods:
- Exploration of self-supervised learning techniques.
- Application analysis across electronic health records, medical images, bioelectrical signals, and genomic/proteomic data.
- Consideration of multimodal dataset integration.
Main Results:
- Self-supervised learning (SSL) presents advantages for leveraging unannotated medical data.
- SSL models show potential in analyzing electronic health records, medical images, bioelectrical signals, and genetic/protein data.
- Challenges include data bias and collection for multimodal SSL applications.
Conclusions:
- Self-supervised learning can significantly accelerate the creation of medical artificial intelligence.
- Addressing data bias is crucial for the effective and equitable implementation of SSL in healthcare.
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