How Generative AI Will Redefine Radiology Reporting

A panel discussion exploring the potential of generative AI to transform radiology reporting and how the technology is already changing radiologists’ workflows. The panel examines opportunities to automate repetitive tasks, improve reporting efficiency and reduce burnout, while also addressing the challenges of adoption, including bias, trust, security and education.

Objectives:

• Explore current and emerging applications of generative AI in radiology reporting and clinical workflows.
• Understand how Gen AI can automate repetitive tasks, improve efficiency and allow radiologists to focus on higher-value clinical work.
• Examine the potential impact of Gen AI on radiologist burnout, report quality, communication and patient care.
• Discuss barriers to responsible adoption, including bias, automation bias, algorithmic aversion, security and trust.
• Consider how education and thoughtful implementation can help radiologists effectively integrate GenAI into practice.

Speakers:

• Woojin Kim, MD, former Chief Medical Information Officer, Rad AI
• William Boonn, MD, former Chief Medical Officer, Rad AI
• Mindy Yang, MD, Radiologist, Jefferson Health
• Elizabeth Hawk, MD, PhD, Radiologist, Stanford Health Care