Algorithm fairness in artificial intelligence for medicine and healthcare
By Richard J. Chen, Judy J. Wang, Drew F. K. Williamson, Tiffany Y. Chen, Jana Lipková, Ming Y. Lu, S. Sahai, Faisal Mahmood. Nature Biomedical Engineering, 2023
550 citations recorded by the source corpus.
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. This Perspective discusses fairness in machine learning and how algorithmic biases arising in clinical workflows can cause healthcare disparities.
Subfields: Artificial Intelligence, Health Informatics, Health Information Management
View Paper: https://www.semanticscholar.org/paper/d85d7a205dfdde6cc41f46141756d14348557218
Citations in the corpus
- Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook (2024, unknown): “It is a careful balance of debiasing and enhancing the models, requiring ongoing monitoring and adjustment to align with ethical standards, particularly in the sensitive domain of health care [185].”
- A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, unknown): “Current common practices to improve AI fairness in the Healthcare domain focus on pre-processing, in-processing, and post-processing [187].”
- Artificial intelligence for digital and computational pathology (2023, unknown): “This approach is particularly”