Deep Learning in Healthcare: A Technical Guide Spanning Imaging, NLP, and Genomics
This review from Nature Medicine provides a structured overview of deep learning techniques applicable to healthcare, covering computer vision for medical imaging, natural language processing for electronic health records, reinforcement learning for robotic surgery, and generalised methods for genomics. It serves as a foundational reference for understanding how neural network architectures can be deployed across the diagnostic pipeline from image acquisition to clinical decision-making.
The original study
A guide to deep learning in healthcare.
- Authors
- Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al.
- Journal
- Nature medicine
- Type
- Journal Article, Review
- PMID
- 30617335
Original abstract
Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.