AI in Radiology: Are Radiologic Technologists Being Replaced or Redefined?

If you’ve ever felt your stomach drop when someone mentions AI in your department, this episode is for you.

Chaun sits down with Jordan Hermiller, MHA, RT(R)(QM), CPHQ — a radiologic technologist and Technical Manager at Agfa Radiology Solutions — to cut through the noise around AI in radiography. Jordan has been a working rad tech, a radiology manager, a university instructor, and a published researcher. Now he works at the intersection of clinical practice and real-world AI implementation. He gets both sides.

In this episode:

• Why AI feels so dominant in radiology right now — and what’s actually driving it

• The difference between real AI and rebranded automation — and how to tell them apart

• What “autonomous x-ray” actually means in a real clinical environment

• How workforce shortages and AI intersect — and what that means for your job

• New roles opening up for radiologic technologists as AI becomes standard

• When to trust an AI recommendation — and when to push back

This episode is brought to you in paid partnership with Agfa Radiology Solutions.

Connect with Jordan Hermiller: LinkedIn

Learn more about Agfa Radiology Solutions: Website Link

AI (CE) Webinar: https://agfaradiologysolutions.com/campaigns/https-agfaradiologysolutions-com-ai_impact_in_radiography/

RadX Case Competition: https://agfaradiologysolutions.com/campaigns/radx-radiology-administration-challenge/

Agfa Website: https://agfaradiologysolutions.com/

Links referenced in this episode:

This episode was created in paid partnership with Agfa Radiology Solutions. All opinions expressed by Chaundria Singleton are her own.

Keywords: AI in radiology, radiologic technologists and AI, autonomous X-ray technology, AI job impact in radiology, future of radiology jobs, AI implementation in imaging, radiology workforce shortage, AI and patient care, technology in radiology, Agfa Radiology Solutions, AI education for technologists, radiology imaging advancements, deep learning in healthcare, AI misconceptions in radiology, quality assurance in radiography, challenges of AI in radiology, AI and patient outcomes, continuing education for radiology techs, radiology automation, AI training data and accuracy.