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Keith J. Dreyer
Researcher at Harvard University
Publications - 108
Citations - 3490
Keith J. Dreyer is an academic researcher from Harvard University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 23, co-authored 86 publications receiving 2348 citations. Previous affiliations of Keith J. Dreyer include American College of Radiology & Partners HealthCare.
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
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
TL;DR: Artificial intelligence offers a new and promising set of methods for analyzing image data, and radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
Journal ArticleDOI
Effect of computerized order entry with integrated decision support on the growth of outpatient procedure volumes: seven-year time series analysis.
Christopher L. Sistrom,Pragya A. Dang,Jeffrey B. Weilburg,Keith J. Dreyer,Daniel I. Rosenthal,James H. Thrall +5 more
TL;DR: Substantial decreases in the growth of outpatient CT and US procedure volume coincident with ROE implementation (supplemented by DS for CT) were observed and the utilization of outpatient MR imaging decreased less impressively.
Journal ArticleDOI
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.
Journal ArticleDOI
Federated learning for predicting clinical outcomes in patients with COVID-19.
Ittai Dayan,Holger R. Roth,Aoxiao Zhong,Ahmed Harouni,Amilcare Gentili,Anas Z. Abidin,Andrew Liu,Anthony Costa,Bradford J. Wood,Chien-Sung Tsai,Chih-Hung Wang,Chun-Nan Hsu,C. K. Lee,Peiying Ruan,Daguang Xu,Dufan Wu,Eddie Huang,Felipe Kitamura,Griffin Lacey,Gustavo César de Antônio Corradi,Gustavo Nino,Hao-Hsin Shin,Hirofumi Obinata,Hui Ren,Jason C. Crane,Jesse Tetreault,Jiahui Guan,John Garrett,Joshua D. Kaggie,Jung Gil Park,Keith J. Dreyer,Krishna Juluru,Kristopher Kersten,Marcio Aloisio Bezerra Cavalcanti Rockenbach,Marius George Linguraru,Marius George Linguraru,Masoom A. Haider,Masoom A. Haider,Meena AbdelMaseeh,Nicola Rieke,Pablo F. Damasceno,Pedro Mário Cruz e Silva,Pochuan Wang,Sheng Xu,Shuichi Kawano,Sira Sriswasdi,Soo-Young Park,Thomas M. Grist,Varun Buch,Watsamon Jantarabenjakul,Watsamon Jantarabenjakul,Weichung Wang,Won Young Tak,Xiang Li,Xihong Lin,Young Joon Kwon,Abood Quraini,Andrew Feng,Andrew N. Priest,Baris Turkbey,Benjamin S. Glicksberg,Bernardo Bizzo,Byung Seok Kim,Carlos Tor-Díez,Chia-Cheng Lee,Chia-Jung Hsu,Chin Lin,Chiu-Ling Lai,Christopher P. Hess,Colin B. Compas,Deepeksha Bhatia,Eric K. Oermann,Evan Leibovitz,Hisashi Sasaki,Hitoshi Mori,Isaac Yang,Jae Ho Sohn,Krishna Nand Keshava Murthy,Li-Chen Fu,Matheus Ribeiro Furtado de Mendonça,Mike Fralick,Min Kyu Kang,Mohammad Adil,Natalie Gangai,Peerapon Vateekul,Pierre Elnajjar,Sarah E Hickman,Sharmila Majumdar,Shelley McLeod,Sheridan Reed,Stefan Gräf,Stephanie Harmon,Tatsuya Kodama,Thanyawee Puthanakit,Thanyawee Puthanakit,Tony Mazzulli,Tony Mazzulli,Vitor Lavor,Yothin Rakvongthai,Yu Rim Lee,Yuhong Wen,Fiona J. Gilbert,Mona Flores,Quanzheng Li +103 more
TL;DR: In this article, the authors used federated learning to predict future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays.