Algorithmic fairness in artificial intelligence for medicine and healthcare

Richard J Chen; Judy J Wang; Drew F K Williamson; Tiffany Y Chen; Jana Lipkova; Ming Y Lu; Sharifa Sahai; Faisal Mahmood

doi:10.1038/s41551-023-01056-8

Algorithmic fairness in artificial intelligence for medicine and healthcare

Nat Biomed Eng. 2023 Jun;7(6):719-742. doi: 10.1038/s41551-023-01056-8. Epub 2023 Jun 28.

Authors

Richard J Chen^{1

2

3

4}, Judy J Wang^{1

5}, Drew F K Williamson^{1

3}, Tiffany Y Chen^{1

3}, Jana Lipkova^{1

2

3}, Ming Y Lu^{1

3

4

6}, Sharifa Sahai^{1

2

3

7}, Faisal Mahmood^{8

9

10

11

12}

Affiliations

¹ Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
² Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
³ Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA.
⁴ Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
⁵ Boston University School of Medicine, Boston, MA, USA.
⁶ Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
⁸ Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. FaisalMahmood@bwh.harvard.edu.
⁹ Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA. FaisalMahmood@bwh.harvard.edu.
¹⁰ Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA. FaisalMahmood@bwh.harvard.edu.
¹¹ Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. FaisalMahmood@bwh.harvard.edu.
¹² Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA. FaisalMahmood@bwh.harvard.edu.

Abstract

In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how patients are diagnosed, treated and billed. In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities. We also review emerging technology for mitigating biases via disentanglement, federated learning and model explainability, and their role in the development of AI-based software as a medical device.

Publication types

Review
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Artificial Intelligence*
Delivery of Health Care
Humans
Machine Learning
Medicine*
Software

Abstract

Publication types

MeSH terms

Grants and funding