AI in Radiology: What's New, What's Holding It Back, and Where It's Heading

AI in radiology is advancing with multimodal models and regulatory progress, but real-world adoption lags due to validation gaps, workflow integration issues, and reimbursement uncertainty (exact figures not reported in source).

• This is a news overview from Diagnostic Imaging, not an original study; no patient-level data.
• The article highlights current obstacles: limited prospective validation, poor interoperability, and unclear reimbursement pathways.
• Emerging trends include foundation models and increased FDA clearances, pointing toward broader clinical integration.

RadPigeon summaries are original and for information only. They are not clinical advice.