Vision 20/20: Automation and advanced computing in clinical radiation oncology

Kevin L. Moore, George C. Kagadis, Todd R. McNutt, Vitali Moiseenko, Sasa Mutic

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


This Vision 2020 paper considers what computational advances are likely to be implemented in clinical radiation oncology in the coming years and how the adoption of these changes might alter the practice of radiotherapy. Four main areas of likely advancement are explored: cloud computing, aggregate data analyses, parallel computation, and automation. As these developments promise both new opportunities and new risks to clinicians and patients alike, the potential benefits are weighed against the hazards associated with each advance, with special considerations regarding patient safety under new computational platforms and methodologies. While the concerns of patient safety are legitimate, the authors contend that progress toward next-generation clinical informatics systems will bring about extremely valuable developments in quality improvement initiatives, clinical efficiency, outcomes analyses, data sharing, and adaptive radiotherapy.

Original languageEnglish
Article number010901
JournalMedical physics
Issue number1
StatePublished - Jan 2014


  • aggregate data analysis
  • automation
  • clinical radiation oncology
  • cloud computing
  • data security
  • machine learning
  • parallel computation
  • quality improvement


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