AI adoption in radiology requires clinician oversight, EHR auditability, and payment alignment

AI tools improve diagnostic accuracy & efficiency in radiology, but adoption stalls without reimbursement, clinician oversight, and EHR integration. Barriers: implementation costs, bias, limited generalizability. Enablers: clinician-in-loop review, auditable outputs, payment ali…

• Adoption was hindered by absent reimbursement for clinician-reviewed AI outputs.
• Implementation and monitoring costs fall on health systems, risking widened disparities.
• Enabling conditions: clinician-in-the-loop review, auditable outputs, equity-centered validation.

Automated summary

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