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What are the ethical issues surrounding AI in healthcare?

Healthcare AI raises profound ethical questions requiring careful navigation. Algorithmic bias represents perhaps the most concerning issue: AI trained predominantly on data from certain demographic groups may perform poorly for underrepresented populations, potentially exacerbating health disparities. Studies have documented racial bias in algorithms used for healthcare resource allocation and kidney function assessment. Autonomy and informed consent challenges arise when patients don't understand how AI influences their care. Should patients be told when AI contributed to diagnosis? Can they refuse AI-assisted care? Transparency vs. proprietary protection creates tensions—companies claim algorithms as trade secrets while patients deserve to understand factors affecting their treatment. Accountability questions multiply: if AI-assisted diagnosis is wrong, who bears responsibility—the AI developer, healthcare system, or individual clinician? Current liability frameworks struggle with distributed responsibility. Overreliance risk means clinicians might defer to AI recommendations even when clinical judgment suggests otherwise, creating automation bias. Data privacy concerns intensify as AI requires vast training data: how should patient data be used, shared, and protected? Can patients opt out of having data used for AI development while still receiving AI-enhanced care? Access and equity issues emerge as expensive AI tools may be available only to well-resourced healthcare systems, widening care quality gaps. Mitigation requires: diverse training data, bias testing, transparent algorithms, clear accountability frameworks, patient education, clinician training emphasizing AI as tool not replacement, and robust data governance protecting privacy while enabling innovation.