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

doi:10.1016/j.acra.2021.08.002

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

Radiology

Research output: Contribution to journal › Article › peer-review

4 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 language	English (US)
Pages (from-to)	119-128
Number of pages	10
Journal	Academic radiology
Volume	29
Issue number	1
DOIs	https://doi.org/10.1016/j.acra.2021.08.002
State	Published - Jan 2022

Bibliographical note

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

Access

10.1016/j.acra.2021.08.002

OpenUrl availability

Full text

Cite this

@article{6e6d1c55ad0444ae870d311505304cd2,

title = "AUR-RRA Review: Logistics of Academic-Industry Partnerships in Artificial Intelligence",

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.",

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",

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

note = "Publisher Copyright: {\textcopyright} 2021 The Association of University Radiologists",

year = "2022",

month = jan,

doi = "10.1016/j.acra.2021.08.002",

language = "English (US)",

volume = "29",

pages = "119--128",

journal = "Academic radiology",

issn = "1076-6332",

publisher = "Elsevier USA",

number = "1",

}

TY - JOUR

T1 - AUR-RRA Review

T2 - Logistics of Academic-Industry Partnerships in Artificial Intelligence

AU - Spilseth, Benjamin

AU - McKnight, Colin D.

AU - Li, Matthew D.

AU - Park, Christian J.

AU - Fried, Jessica G.

AU - Yi, Paul H.

AU - Brian, James M.

AU - Lehman, Constance D.

AU - Wang, Xiaoqin Jennifer

AU - Phalke, Vaishali

AU - Pakkal, Mini

AU - Baruah, Dhiraj

AU - Khine, Pwint Phyu

AU - Fajardo, Laurie L.

PY - 2022/1

Y1 - 2022/1

N2 - 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.

AB - 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.

KW - academic radiology

KW - academic-industry collaborations

KW - academic-industry partnerships

KW - and computer assisted diagnosis

KW - artificial intelligence

KW - challenges

KW - clinical data ownership

KW - deep learning

KW - machine learning

KW - opportunities

KW - pitfalls

UR - http://www.scopus.com/inward/record.url?scp=85115378381&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85115378381&partnerID=8YFLogxK

U2 - 10.1016/j.acra.2021.08.002

DO - 10.1016/j.acra.2021.08.002

M3 - Article

C2 - 34561163

AN - SCOPUS:85115378381

SN - 1076-6332

VL - 29

SP - 119

EP - 128

JO - Academic radiology

JF - Academic radiology

IS - 1

ER -

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

Abstract

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this