Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias

Thomas Hofweber; Rebecca L Walker

doi:10.18043/001c.120562

Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias

N C Med J. 2024 Jun;85(4):240-245. doi: 10.18043/001c.120562.

Authors

Thomas Hofweber¹, Rebecca L Walker²

Affiliations

¹ Department of Philosophy, University of North Carolina at Chapel Hill.
² Center for Bioethics, University of North Carolina at Chapel Hill.

PMID: 39466090
DOI: 10.18043/001c.120562

Abstract

Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.

Keywords: Thomas Hofweber; artificial intelligence; equity; health care; health policy; machine learning; model interpretability; technology.

Publication types

Review

MeSH terms

Bias
Confidentiality / ethics
Delivery of Health Care / ethics
Humans
Machine Learning* / ethics
Privacy