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Cited by 420Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Shifting machine learning for healthcare from development to deployment and from models to data

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Zhang, Angela, Xing, Lei, Zou, James, Wu, Joseph C.·2022·Nature Biomedical Engineering
DOI 10.1038/s41551-022-00898-yPMC PMC12063568
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Abstract

In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance.

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Cited by

Sorted by citations

Authors
Zhang, A., Xing, L., Zou, J., & Wu, J. C.
Affiliations
Stanford Medicine (US)
Corresponding author
  • Zhang, Angela
  • Wu, Joseph C.
ORCID
Venue
Nature Biomedical Engineering
Publisher
Springer Science and Business Media LLC
Published
2022-07-04
Volume
6
Issue
12
Pages
1330-1345
ISSN
2157-846X (ISSN-L)
DOI
10.1038/s41551-022-00898-y
PMC
PMC12063568
OpenAlex
W4283801824
Cited by
420
References
33
Citations by year
2026: 77 · 2025: 139 · 2024: 133 · 2023: 60 · 2022: 11
FWCI
5.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Biomedical Engineering (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (F30 HL156478, R01HL130020, R01HL146690, R01HL126527)
  • U.S. Department of Health & Human Services | NIH | National Cancer Institute (1R01CA227713, 1R01CA256890)
  • National Science Foundation (CAREER1942926)
  • U.S. Department of Health & Human Services | NIH | National Institute on Aging (P30AG059307)
  • U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (U01MH098953)
  • American Heart Association (17MERIT3361009)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
machine learning in healthcare, clinical data, model deployment, deep generative models, federated learning, data augmentation, transformer models, clinical prediction, data-centric machine learning
MeSH terms
Machine Learning, Delivery of Health Care, Electric Power Supplies

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