Radiology Quality Assurance

Radiology quality assurance (QA) aims to improve the accuracy and efficiency of radiological diagnoses. Current research focuses on leveraging artificial intelligence, particularly combining AI-based image analysis with natural language processing of radiology reports, to automate QA processes. This approach, exemplified by systems that flag discrepancies for human review, significantly reduces the workload associated with traditional QA methods, potentially improving both the speed and accuracy of detecting missed findings. The ultimate goal is to enhance patient care through more efficient and reliable diagnostic workflows.