Big Data and Machine Learning—Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference

Jonathan B. Kruskal, Seth Berkowitz, J. Raymond Geis, Woojin Kim, Paul Nagy, Keith Dreyer

Research output: Contribution to journalArticlepeer-review

Abstract

The 38th radiology Intersociety Committee reviewed the current state and future direction of clinical data science and its application to radiology practice. The assembled participants discussed the need to use current technology to better generate and demonstrate radiologists’ value for our patients and referring providers. The attendants grappled with the potentially disruptive applications of machine learning to image analysis. Although the prospect of algorithms’ interpreting images automatically initially shakes the core of the radiology profession, the group emerged with tremendous optimism about the future of radiology. Emerging technologies will provide enormous opportunities for radiologists to augment and improve the quality of care they provide to their patients. Radiologists must maintain an active role in guiding the development of these technologies. The conference ended with a call to action to develop educational strategies for future leaders, communicate optimism for our profession's future, and engage with industry to ensure the ethics and clinical relevance of developing technologies.

Original languageEnglish (US)
Pages (from-to)811-817
Number of pages7
JournalJournal of the American College of Radiology
Volume14
Issue number6
DOIs
StatePublished - Jun 2017

Keywords

  • ACR
  • Intersociety Committee
  • big data
  • data science
  • deep learning
  • imaging informatics
  • machine learning
  • radiology

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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