Bias recognition and mitigation strategies in artificial intelligence healthcare applications
Fereshteh Hasanzadeh, Colin B. Josephson, G Waters, Demilade Adedinsewo, Zahra Azizi, James A. White
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.
Topics & Concepts
Health careComputer scienceArtificial intelligencePsychologyPolitical scienceLawArtificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIHealthcare Technology and Patient Monitoring