Abstract
The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 123-144 |
| Number of pages | 22 |
| Journal | Annual review of biomedical data science |
| Volume | 4 |
| DOIs | |
| State | Published - May 6 2021 |
| Externally published | Yes |
Keywords
- bias
- ethics
- health
- health disparities
- healthcare
- machine learning
ASJC Scopus subject areas
- Biomedical Engineering
- Biochemistry, Genetics and Molecular Biology (miscellaneous)
- Genetics
- Cancer Research
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