Bias recognition and mitigation strategies in artificial intelligence healthcare applications

Fereshteh Hasanzadeh; Colin B Josephson; Gabriella Waters; Demilade Adedinsewo; Zahra Azizi; James A White

doi:10.1038/s41746-025-01503-7

Bias recognition and mitigation strategies in artificial intelligence healthcare applications

NPJ Digit Med. 2025 Mar 11;8(1):154. doi: 10.1038/s41746-025-01503-7.

Authors

Fereshteh Hasanzadeh¹, Colin B Josephson², Gabriella Waters³, Demilade Adedinsewo⁴, Zahra Azizi⁵, James A White^{6

7}

Affiliations

¹ Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
² Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
³ Morgan State University, Center for Equitable AI & Machine Learning Systems, Baltimore, MD, USA.
⁴ Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA.
⁵ Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
⁶ Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. jawhit@ucalgary.ca.
⁷ Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. jawhit@ucalgary.ca.

Abstract

Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance.

Publication types

Review