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The integration of Artificial Intelligence (AI) in healthcare presents transformative opportunities alongside ethical challenges, particularly the introduction of biases. This chapter explores how AI inherits and amplifies human biases, impacts patient care, and flouts ethical principles. Biases appear in data used for AI data sets and AI algorithms, posing risks of delivering biased results for the AI tools used in healthcare. Case studies illustrate real-world consequences, such as racial bias in diagnostic algorithms and gender disparities in treatment recommendations. Practical strategies for reducing biases, including diverse data collection and fairness-aware algorithms, are proposed. Ethical frameworks and multidisciplinary collaboration are essential to ensure AI systems promote fairness, transparency, and equitable healthcare outcomes.

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Responsible Use of AI: Ethics, Biases, and Fairness

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  • 发表日期：

    2025年

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  • Karen Blades

摘要

The integration of Artificial Intelligence (AI) in healthcare presents transformative opportunities alongside ethical challenges, particularly the introduction of biases. This chapter explores how AI inherits and amplifies human biases, impacts patient care, and flouts ethical principles. Biases appear in data used for AI data sets and AI algorithms, posing risks of delivering biased results for the AI tools used in healthcare. Case studies illustrate real-world consequences, such as racial bias in diagnostic algorithms and gender disparities in treatment recommendations. Practical strategies for reducing biases, including diverse data collection and fairness-aware algorithms, are proposed. Ethical frameworks and multidisciplinary collaboration are essential to ensure AI systems promote fairness, transparency, and equitable healthcare outcomes.