Overcoming struggles to define algorithmic fairness in healthcare

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Overcoming struggles to define algorithmic fairness in healthcare

The article discusses the challenges in defining algorithmic fairness in healthcare AI, emphasizing the need for a collaborative and nuanced approach. Medical ethicists argue that a universal definition of fairness is elusive, impacting the advancement of patient outcomes.

Researchers from Stanford and Emory University highlight the complexities of defining fairness in AI models, especially in the context of health equity. Collaborative efforts involving patients, providers, and developers are suggested to improve transparency and fairness in algorithm development.

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