Document Type
Article
Publication Title
Genetics in Medicine
Publication Date
1-6-2023
ISSN
1098-3600
Page Number
100006
Keywords
Common variants, Family history, Genotyping, Monogenic risks, Polygenic risk scores
Disciplines
Computer Law | Health Law and Policy | Law
Abstract
The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care
Recommended Citation
Ellen W. Clayton, Jodell E. Linder, Aimee Allworth, Sara T. Bland, and 100 others...,
Returning Integrated Genomic Risk and Clinical Recommendations: The eMERGE Study, 25 Genetics in Medicine. 100006
(2023)
Available at: https://scholarship.law.vanderbilt.edu/faculty-publications/1330