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Introduction

Published online by Cambridge University Press:  21 April 2022

Edited by
Sze-chuan Suen ,
David Scheinker  and
Eva Enns
Sze-chuan Suen
Affiliation:
University of Southern California
David Scheinker
Affiliation:
Stanford University, California
Eva Enns
Affiliation:
University of Minnesota
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Artificial Intelligence for Healthcare
Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
 , pp. 1 - 12
Publisher: Cambridge University Press
Print publication year: 2022

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  • Introduction
  • Edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, California, Eva Enns, University of Minnesota
  • Book: Artificial Intelligence for Healthcare
  • Online publication: 21 April 2022
  • Chapter DOI: https://doi.org/10.1017/9781108872188.002
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To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Introduction
  • Edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, California, Eva Enns, University of Minnesota
  • Book: Artificial Intelligence for Healthcare
  • Online publication: 21 April 2022
  • Chapter DOI: https://doi.org/10.1017/9781108872188.002
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Introduction
  • Edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, California, Eva Enns, University of Minnesota
  • Book: Artificial Intelligence for Healthcare
  • Online publication: 21 April 2022
  • Chapter DOI: https://doi.org/10.1017/9781108872188.002
Available formats
×