Jun 13, 2025
A panel discussion on "Leveraging Operational AI to Improve the Patient Experience" took place at the recent SIIM25 conference. This panel featured radiology AI thought leaders Dr. Ryan K. Lee, Radiology Chair at Jefferson Einstein, Dr. Tessa Cook, Vice-Chair of Radiology Practice Transformation at Penn and Dr. Andrew Del Gaizo, Chief Medical Information Officer at Rad AI and was moderated by Dr. Steven Blumer, Americas Director, Radiology Digital Medical Affairs at Bayer Radiology. The discussion provided invaluable insights into the potential impact of operational AI on radiology workflows and enhancing patient experiences.
Understanding Operational AI
Operational AI, often referred to as non-pixel-based AI, is designed to streamline radiology workflows, enhancing efficiency and the overall patient experience. Unlike pixel-based AI, which focuses on image analysis, operational AI integrates into various stages of the radiology workflow, from order entry and clinical decision support all the way to radiology reporting.
Key Takeaways from the Discussion
The panel highlighted several aspects of operational AI to improve the patient experience. In particular, the panel touched on operational AI’s potential to expedite examinations, with a particular focus on speeding up MR exams, which significantly reduces exam times and can enhance patient satisfaction.
Other operational AI uses cases discussed were patient adherence and assistance with radiology reporting. Operational AI aids in improving adherence to follow-up imaging recommendations, helping to ensure patients receive timely and appropriate care. Additionally, the enhanced reporting efficiency brought by operational AI can for quicker report turn-around-times.
Transforming radiology reports into formats that are easy to understand, complete with diagrams, was highlighted as another use case of operational AI. This approach helps patients better understand their radiology reports and potentially play a more active role in their medical care.
The panel also noted that successful implementation of AI requires collaboration across diverse stakeholders, both clinical and nonclinical, spanning multiple departments within an institution. The panel underscored the importance of such collaborative efforts to fully realize the benefits of operational AI in healthcare.
The panel discussion underscored the importance of operational AI in helping to enhance radiology workflows and the patient experience. By integrating AI into various stages of the radiology workflow, healthcare institutions may achieve improved efficiency, better patient adherence, and more accessible radiology reports. The insights shared by Drs. Lee, Cook, and Del Gaizo can serve as guidance for healthcare providers eager to embrace the future of operational AI.
