HOTL Model Proposed for Postdeployment AI Monitoring in Radiology

RadioGraphics review proposes human-on-the-loop (HOTL) as pragmatic oversight model for radiology AI, enabling proactive postdeployment monitoring via temporal stability, predictive divergence, and uncertainty quantification to balance safety and scaling.

• Narrative review describes human-AI oversight spectrum (HITL to HOOTL) and advocates HOTL for high-stakes imaging.
• Monitoring uses temporal stability of inputs/outputs and predictive divergence from baseline, avoiding immediate ground-truth need; uncertainty quantification prioritizes reviews.
• Proposed threshold alerts, tiered escalation, and root cause analysis differentiate AI degradation from data/pipeline issues.

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