How cognitive machines can augment medical imaging

D. Douglas Miller, Eric W. Brown

Research output: Contribution to journalReview articlepeer-review

21 Scopus citations


OBJECTIVE. Artificial intelligence (AI) neural networks rapidly convert disparate facts and data into highly predictive analytic models. Machine learning maps image-patient phenotype correlations opaque to standard statistics. Deep learning performs accurate image-derived tissue characterization and can generate virtual CT images from MRI datasets. Natural language processing reads medical literature and efficiently reconfigures years of PACS and electronic medical record information. CONCLUSION. AI logistics solve radiology informatics workflow pain points. Imaging professionals and companies will drive health care AI technology insertion. Data science and computer science will jointly potentiate the impact of AI applications for medical imaging.

Original languageEnglish (US)
Pages (from-to)9-14
Number of pages6
JournalAmerican Journal of Roentgenology
Issue number1
StatePublished - Jan 2019


  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Natural language processing
  • Technology insertion

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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