Demystifying AI in Clinical Practice

AI Takes Radiology from Diagnosis to Prevention

8 min · 5. maalis 2026
jakson AI Takes Radiology from Diagnosis to Prevention kansikuva

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From the Applied Radiology booth at RSNA, one theme surfaced repeatedly across conversations: artificial intelligence is no longer just about reading images faster—it is reshaping how radiology contributes to patient care, access, and population health. That message came through clearly during a live discussion between Kieran Anderson, Group Publisher at Applied Radiology, and Suzie Bash, MD, neuroradiologist and Medical Director at RadNet.

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jakson AI in Radiology: Isolated Algorithms to Scalable Clinical Impact kansikuva

AI in Radiology: Isolated Algorithms to Scalable Clinical Impact

Artificial intelligence in radiology is often discussed in broad, aspirational terms, but far less attention is paid to what happens after algorithms are cleared, purchased, and deployed. In a recent discussion hosted by Applied Radiology, experts examined how AI is being implemented at scale and what it takes to translate technical capability into meaningful clinical impact. During the conversation, Avi Sharma, MD, host of Applied Radiology’s AI Podcast was joined by co-host Lawrence Tanenbaum, MD, and Greg Sorenson, MD, Chief Science Officer at RadNet, and, to explore how AI moves from isolated tools to enterprise-level infrastructure. The discussion focused less on individual algorithms and more on workflow, adoption, and sustainability in real-world imaging environments

5. maalis 202617 min