J
J. Raymond Geis
Researcher at University of Colorado Denver
Publications - 17
Citations - 1723
J. Raymond Geis is an academic researcher from University of Colorado Denver. The author has contributed to research in topics: Imaging informatics & Ethical code. The author has an hindex of 14, co-authored 17 publications receiving 1073 citations. Previous affiliations of J. Raymond Geis include University of Colorado Boulder & American College of Radiology.
Papers
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Journal ArticleDOI
Current Applications and Future Impact of Machine Learning in Radiology.
Garry Choy,Omid Khalilzadeh,Omid Khalilzadeh,Mark Michalski,Synho Do,Anthony E. Samir,Oleg S. Pianykh,J. Raymond Geis,Pari V. Pandharipande,James A. Brink,Keith J. Dreyer +10 more
TL;DR: Examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology and the future impact and natural extension of these techniques in radiology practice are discussed.
Journal ArticleDOI
Implementing Machine Learning in Radiology Practice and Research.
TL;DR: In this article, the authors describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation.
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Machine Learning in Radiology: Applications Beyond Image Interpretation
Paras Lakhani,Adam Prater,R. Kent Hutson,Kathy P. Andriole,Keith J. Dreyer,José M. Morey,Luciano M. Prevedello,Toshi J. Clark,J. Raymond Geis,Jason N. Itri,C. Matthew Hawkins +10 more
TL;DR: An overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation is described, to help radiology practices prepare for the future and realize performance improvement and efficiency gains.
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Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.
J. Raymond Geis,J. Raymond Geis,Adrian P. Brady,Carol C. Wu,Jack Spencer,Erik Ranschaert,Jacob L. Jaremko,Steve G. Langer,Andrea Borondy Kitts,Judy Birch,William F. Shields,Robert van den Hoven van Genderen,Elmar Kotter,Judy Wawira Gichoya,Tessa S. Cook,Matthew B. Morgan,An Tang,Nabile M. Safdar,Marc D. Kohli +18 more
TL;DR: It is agreed that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner and the radiology community should start now to develop codes of ethics and practice for AI.
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Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session
TL;DR: There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data and high-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described.