“Increased Desire to Embrace the Future with AI”: Insights from AUR and recent Radiology Conferences from Kyle Souligne, Director of Enterprise Imaging, Radiology.

The Association of University Radiologists (AUR) recently held a meeting in Boston where attendees collaborated and shared ideas on how to solve clinical problems. I was lucky enough to attend a think tank session where we discussed innovative ways industry partners and clinicians can work together to make the most of our resources. Throughout this session, and the rest of the meeting, it became clear that Artificial Intelligence (AI) is becoming increasingly important in the field of radiology to help overcome the hurdles around increasing volumes and staffing shortages.

Many of my recent conversations with attendees of other associations, and with counterparts from the industry, all shared a common theme; more focus needs to be made on how AI must be implemented in ways that make clinicians most efficient. It is becoming less and less that radiologists fear that AI will be used to replace their practice. The true need is to implement AI in ways that make radiologists more efficient, allowing them to read and handle the volume of studies required and effectively support more coordinate care for patients.

While there is great promise for AI to be used to discover incidental findings more efficiently and support physicians in diagnosing patients, the most immediate need continues to circle around not providing more information, but supporting more efficient work. Using AI to categorize, prioritize, and distribute studies to the most appropriate reader are all hot topics, but often are met with challenges around effective workflow adoption. These struggles are generating increased focus on the implementation science of how we are using AI so we can most effectively leverage it and overcome the adoption hurdles we face.

AGFA HealthCare is at the forefront of implementing AI in radiology in ways to make the most of the algorithm’s power. RUBEE® AI was designed with the focus to embed AI tools natively inside the PACS and enterprise viewer to reduce “swivel chair” for radiologists. This means that radiologists don’t need to switch between different systems to use AI. The RUBEE® engine can also help leverage AI findings to categorize, prioritize, and distribute studies to the most appropriate reader, making the process more efficient. Anjum Ahmed, CMO at AGFA HealthCare, presented this approach at AUR, and many attendees applauded it.

The increasing importance of AI in radiology is hyper-evident with the growing focus on the need to implement these algorithms in ways that make radiologists more efficient. By doing so, we can help lessen the burden of radiologists and at the same time help improve the delivery of patient care. AGFA HealthCare is leading the way in implementing AI in radiology, and our approach was well received at AUR. While we are still in a period of transition, we are excited to be working with so many health systems as they move towards a future where AI is embraced and utilized to better serve patients and the healthcare system.

Are you intrigued by the potential of AI in healthcare imaging? We’d love to hear from you. Let’s start a conversation.