Clinical AI deployment lessons: validation and monitoring on platforms

Real-world AI in radiology faces a deployment gap where performance drops. This review discusses continuous monitoring on clinical platforms to maintain utility.

• AI algorithms validated in controlled settings often underperform when deployed across diverse clinical environments, a phenomenon termed the deployment gap.
• Platform-based deployment enables continuous monitoring of AI performance, feedback, and adaptation to local data and practices.
• The narrative review summarizes strategies for clinical validation and post-deployment surveillance to close the gap.

Automated summary

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