What are the key challenges in implementing AI in healthcare
Implementing AI in healthcare faces significant challenges that span technical, ethical, regulatory, and operational domains. Here’s a breakdown of the key obstacles:
1. Data Quality and Accessibility
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Fragmented and inconsistent data: Healthcare data is often siloed across systems, leading to inaccuracies that impair AI model performance34.
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Privacy and security risks: Sensitive patient data requires stringent protection under regulations like HIPAA, necessitating advanced encryption and governance frameworks245.
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Bias amplification: Training data may reflect historical disparities, leading to biased AI outputs that worsen inequities in care47.
2. Technical Integration
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Legacy system incompatibility: Existing health IT infrastructure (e.g., EHRs) often lacks interoperability with AI tools, requiring costly overhauls16.
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Workflow disruption: Adapting clinical processes to incorporate AI can create inefficiencies if not carefully aligned with provider needs14.
3. Regulatory and Ethical Hurdles
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Compliance …