Quality Metrics and Data-Driven Improvement (2026)-Artificial Intelligence for Quality Improvement in Radiology

Artificial Intelligence for Quality Improvement in Radiology

Video Transcription

Video Summary

The speaker describes how AI can improve radiology quality across the entire workflow, from exam ordering and protocoling to image acquisition, interpretation, reporting, and follow-up. Examples include decision support, dose reduction, worklist prioritization, error detection, and NLP-based report checks. She emphasizes that AI implementation must follow quality improvement principles: governance committees, careful tool selection, phased rollout, continuous monitoring, and retraining to prevent performance drift. AI should enhance human performance, reduce burden, and improve accuracy, clarity, timeliness, and safety in radiology.

Keywords

radiology AI

quality improvement

workflow optimization

decision support

natural language processing