QI: Value in Imaging 1: Value in Radiology | Domain: Quality Improvement in Your Practice (2025)-MSQI3118-2025

Video Transcription

Video Summary

During a session on radiology, several speakers shared insights on the field’s current state and future directions. The first speaker discussed the concept of value in radiology, emphasizing the need for radiologists to adapt to evolving demands. Radiologists are experiencing increased opportunities and innovations, such as AI and robotic interventions, but they face challenges including burnout, unnecessary imaging, and reluctance to embrace value-based approaches. The speaker highlighted the importance of understanding value, which involves providing high-quality, cost-effective care that improves outcomes and patient experiences.<br /><br />To address misunderstandings surrounding radiology reports, another presentation introduced initiatives like Porter, the Patient-Oriented Radiology Reporter. This tool aims to help patients better understand their radiology reports through lay language definitions and supplementary visual aids, thereby improving patient engagement and satisfaction.<br /><br />Lastly, the session covered the impact of AI and machine learning in enhancing the quality and efficiency of radiology services. AI applications range from improving image quality and reducing dosage in imaging procedures to assisting radiologists in diagnostics. Projects like using AI for mammography image quality assessment and prioritizing urgent radiological findings are underway. The need for radiologists to integrate AI into practice is emphasized to enhance service delivery and patient care outcomes.<br /><br />Overall, the session underscored the balance between technological advancement and maintaining a patient-centered approach, highlighting that radiologists need to adapt to technological integrations while focusing on delivering comprehensive value in patient care.

Keywords

radiology

AI

value-based care

patient engagement

machine learning

radiology reports

robotic interventions

burnout

diagnostics

patient-centered approach