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Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias
Thomas Hofweber1, Rebecca L Walker2
1Department of Philosophy, University of North Carolina at Chapel Hill.
North Carolina Medical Journal
|October 28, 2024
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 Hofweberartificial intelligenceequityhealth carehealth policymachine learningmodel interpretabilitytechnologyMore Related Videos
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