Demystifying AI in Clinical Practice

Foundation Models Emerge as the “Electricity” of Radiology AI

26 min · 5. mar. 2026
episode Foundation Models Emerge as the “Electricity” of Radiology AI cover

Beskrivelse

Fans of the PBS television series Downton Abbey, set in early 20th-century England, witnessed the far-reaching effects of the Second Industrial Revolution—particularly innovations in electricity, mechanization, and communication—on the lives of its characters. What fueled this all-encompassing change, and what does it have in common with artificial intelligence in health care?  During a discussion with Nina Kottler, MD, Chief Medical AI Officer at Mosaic Clinical Technologies, Lawrence Tanenbaum, MD, raised the comparison as a way to describe how foundational technologies perhaps, not so quietly, reshape professional life

Kommentarer

0

Vær den første til at kommentere

Tilmeld dig nu og bliv en del af Demystifying AI in Clinical Practice-fællesskabet!

Kom i gang

1 måned kun 9 kr.

Derefter 99 kr. / måned · Opsig når som helst.

  • Podcasts kun på Podimo
  • 20 lydbogstimer pr. måned
  • Gratis podcasts

Alle episoder

8 episoder

episode AI in Radiology: Isolated Algorithms to Scalable Clinical Impact cover

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. mar. 202617 min