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

2 Scopus citations

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)
Pages (from-to)119-128
Number of pages10
JournalAcademic Radiology
Volume29
Issue number1
DOIs
StatePublished - Jan 2022

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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