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

• Fragmented and inconsistent data: Healthcare data is often siloed across systems, leading to inaccuracies that impair AI model performance34.

• Privacy and security risks: Sensitive patient data requires stringent protection under regulations like HIPAA, necessitating advanced encryption and governance frameworks245.

• Bias amplification: Training data may reflect historical disparities, leading to biased AI outputs that worsen inequities in care47.

2. Technical Integration

• Legacy system incompatibility: Existing health IT infrastructure (e.g., EHRs) often lacks interoperability with AI tools, requiring costly overhauls16.

• Workflow disruption: Adapting clinical processes to incorporate AI can create