Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support
This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.
Authors
- Christopher A. Harle (ORCID: https://orcid.org/0000-0002-4803-3632)
- Walid F. Gellad (ORCID: https://orcid.org/0000-0002-6992-5197)
- Khoa A. Nguyen (ORCID: https://orcid.org/0000-0002-0096-0293)
- Debbie L. Wilson (ORCID: https://orcid.org/0000-0002-1640-5497)
- Wei‐Hsuan Lo‐Ciganic (ORCID: https://orcid.org/0000-0001-6590-4770)
- Laura G. Militello (ORCID: https://orcid.org/0000-0001-8445-5640)
- Julie Diiulio (ORCID: https://orcid.org/0000-0002-5791-0705)
Institutions
- University of Pittsburgh (US)
- University of Florida (US)
- Geriatric Research Education and Clinical Center (US)
- North Florida/South Georgia Veterans Health System (US)
- Applied Decision Science (United States) (US)
- VA Pittsburgh Healthcare System (US)
- Indiana University – Purdue University Indianapolis (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1093/jamia/ocae291
- Citations
- 6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.72
Funders
- National Institutes of Health
- National Institute on Drug Abuse