Abstract
monitoring included a live hazard log; RAID (risks, assumptions, issues, dependencies) tracking; user-sentiment surveys; service-activity monitoring; and weekly multidisciplinary team (MTD) reviews of downtime, discordant cases and workflow impact.Across five months, >12,000 radiographic studies were sent for analysis, with 95% receiving a successful AI output.The AI flagged a potential fracture in 22% of processed studies.These high-volume findings demonstrated consistent tool performance at scale.Monitoring identified occasional delays or unprocessed studies, which were resolved through MDT review.Operational indicators, including shifts in fracture-clinic activity and emergency department length of stay, are being evaluated through ongoing audits to understand wider clinical effects of AI-assisted detection.Monitoring was essential for the safe transition of this AI tool from pilot to rollout and into post-implementation use.High-volume, real-world use showed consistent performance.Continuous review of risks and user feedback helped keep the system safe and supported clinician confidence.This provides a practical model for AI vigilance during and after deployment.