Is It Possible for Artificial Intelligence in Healthcare to Achieve Genuine Inclusivity?

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AI in healthcare may exacerbate existing disparities unless equity, diversity, and inclusion are integrated throughout the AI life cycle.
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The EDAI framework offers guidance to incorporate equity, diversity, and inclusion at micro, meso, and macro levels in health care.
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Populations facing barriers to care are often underrepresented in AI training datasets, leading to their invisibility in health algorithms.
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Equity considerations are frequently deprioritized in AI development, often viewed as optional rather than essential for safe AI implementation.
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Implementation challenges in AI systems can widen gaps in care access, particularly for frontline workers with minimal training or support.

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