AUR-RRA Review: Logistics of Academic-Industry Partnerships in Artificial Intelligence

Benjamin Spilseth, Colin D. McKnight, Matthew D. Li, Christian J. Park, Jessica G. Fried, Paul H. Yi, James M. Brian, Constance D. Lehman, Xiaoqin Jennifer Wang, Vaishali Phalke, Mini Pakkal, Dhiraj Baruah, Pwint Phyu Khine, Laurie L. Fajardo

Research output: Contribution to journalArticlepeer-review

Abstract

The Radiology Research Alliance (RRA) of the Association of University Radiologists (AUR) convenes Task Forces to address current topics in radiology. In this article, the AUR-RRA Task Force on Academic-Industry Partnerships for Artificial Intelligence, considered issues of importance to academic radiology departments contemplating industry partnerships in artificial intelligence (AI) development, testing and evaluation. Our goal was to create a framework encompassing the domains of clinical, technical, regulatory, legal and financial considerations that impact the arrangement and success of such partnerships.

Original languageEnglish (US)
JournalAcademic radiology
DOIs
StateAccepted/In press - 2021

Bibliographical note

Funding Information:
M.D.L. reports funding from an RSNA R&E Fund Research Resident/Fellow Grant, outside of the submitted work. P.H.Y. – Consultant and Shareholder, Bunkerhill Health. L.L.F. – Consultant on Artificial Intelligence Development, Hologic, Inc., Danbury, CT.

Publisher Copyright:
© 2021 The Association of University Radiologists

Keywords

  • academic radiology
  • academic-industry collaborations
  • academic-industry partnerships
  • and computer assisted diagnosis
  • artificial intelligence
  • challenges
  • clinical data ownership
  • deep learning
  • machine learning
  • opportunities
  • pitfalls

PubMed: MeSH publication types

  • Journal Article

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