Intelligent imaging: Applications of machine learning and deep learning in radiology

Geoff Currie, Eric Rohren

Research output: Contribution to journalComment/debatepeer-review

1 Scopus citations


Artificial intelligence (AI) in radiology is transforming medical image analysis. While applications in triaging for priority reporting and radiomic feature analysis have been widely reported, perhaps the most important applications lie in noise reduction, image optimization following dose reduction strategies, image reconstruction direct from projection data and generation of pseudo-CT for attenuation correction. There are common beneficial applications, and potential risks, between human radiology and veterinary radiology. Artificial intelligence may see recrafting of some responsibilities but offers AI augmentation of human driven systems. The redundancy afforded by human augmentation of AI and AI autonomy are not on the horizon, but rather are already here.

Original languageEnglish (US)
Pages (from-to)880-888
Number of pages9
JournalVeterinary Radiology and Ultrasound
Issue numberS1
StatePublished - Dec 2022


  • artificial intelligence
  • artificial neural network
  • convolutional neural network
  • deep learning
  • medical imaging
  • radiology

ASJC Scopus subject areas

  • veterinary(all)


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