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Computer Science

D-Index
37
Citations
6283
World Ranking
10694
National Ranking
4470

Overview

Eliot L. Siegel is affiliated with the University of Maryland, Baltimore in the United States. Their research primarily spans the fields of Medicine, with particular contributions to Radiology, Nuclear Medicine and Imaging, Health Informatics, Artificial Intelligence, Pulmonary and Respiratory Medicine, and Biomedical Engineering.

Their work covers a variety of main topics, including:

  • Radiomics and Machine Learning in Medical Imaging
  • Artificial Intelligence in Healthcare and Education
  • Radiology practices and education
  • Advanced X-ray and CT Imaging
  • Medical Imaging Techniques and Applications
  • COVID-19 diagnosis using AI
  • Digital Radiography and Breast Imaging

Siegel has published extensively in the following venues:

  • PET Clinics
  • Journal of the American College of Radiology
  • arXiv (Cornell University)
  • Journal of Nuclear Medicine
  • Academic Radiology

Frequent co-authors associated with Siegel include Babak Saboury, Arman Rahmim, Paul H. Yi, Michael Morris, and Vishwa S. Parekh.

Recent papers authored or co-authored by Siegel include:

  • "Criteria for the translation of radiomics into clinically useful tests" (2022, Nature Reviews Clinical Oncology)
  • "A Brief History of AI: How to Prevent Another Winter (A Critical Review)" (2021, PET Clinics)
  • "Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine" (2020, Current Problems in Diagnostic Radiology)
  • "Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction" (2022, Annual Review of Biomedical Engineering)
  • "Trustworthy Artificial Intelligence in Medical Imaging" (2021, PET Clinics)

Best Publications

  • Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

    Ophir Gozes;Maayan Frid-Adar;Hayit Greenspan;Patrick D. Browning

  • Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment

    Steven E. Dilsizian;Eliot L. Siegel

  • Fast and effective retrieval of medical tumor shapes

    P. Korn;N. Sidiropoulos;C. Faloutsos;E. Siegel

  • Radiology reporting, past, present, and future: the radiologist's perspective.

    Bruce I. Reiner;Nancy Knight;Eliot L. Siegel;Eliot L. Siegel

  • Implementing Virtual and Augmented Reality Tools for Radiology Education and Training, Communication, and Clinical Care.

    Raul N. Uppot;Benjamin Laguna;Colin J. McCarthy;Gianluca De Novi

  • A Brief History of AI: How to Prevent Another Winter (A Critical Review)

    Amirhosein Toosi;Andrea G. Bottino;Babak Saboury;Eliot Siegel

  • Work Flow Redesign: The Key to Success When Using PACS

    Eliot L. Siegel;Bruce I. Reiner

  • Imaging evaluation of penetrating neck injuries.

    Scott D. Steenburg;Clint W. Sliker;Kathirkamanathan Shanmuganathan;Eliot L. Siegel

  • Integrating the Healthcare Enterprise: a primer. Part 1. Introduction.

    Eliot L. Siegel;David S. Channin

  • Informatics in radiology: image exchange: IHE and the evolution of image sharing.

    David S Mendelson;Peter R G Bak;Elliot Menschik;Eliot Siegel

  • An Improved Index of Image Quality for Task-based Performance of CT Iterative Reconstruction across Three Commercial Implementations

    Olav Christianson;Joseph J. S. Chen;Zhitong Yang;Ganesh Saiprasad

  • Making filmless radiology work

    Eliot L. Siegel;John N. Diaconis;Stephen M. Pomerantz;Robert Allman

  • Evolution of the Digital Revolution: A Radiologist Perspective

    Bruce I. Reiner;Bruce I. Reiner;Eliot L. Siegel;Eliot L. Siegel;Khan M. Siddiqui;Khan M. Siddiqui

  • Addressing the Coming Radiology Crisis—The Society for Computer Applications in Radiology Transforming the Radiological Interpretation Process (TRIP™) Initiative

    Katherine P. Andriole;Richard L. Morin;Ronald L. Arenson;John A. Carrino

  • Workflow Optimization: Current Trends and Future Directions

    Bruce I. Reiner;Eliot L. Siegel;John A. Carrino

  • Will machine learning end the viability of radiology as a thriving medical specialty

    Stephen Chan;Eliot L Siegel

  • Effect of film-based versus filmless operation on the productivity of CT technologists.

    B I Reiner;E L Siegel;F J Hooper;D Glasser

  • Reinventing Radiology: Big Data and the Future of Medical Imaging

    Michael A. Morris;Babak Saboury;Brian Burkett;Jackson Gao

  • Radiology reporting: returning to our image-centric roots.

    Bruce Reiner;Eliot Siegel

  • Digital Radiography Reject Analysis: Data Collection Methodology, Results, and Recommendations from an In-depth Investigation at Two Hospitals

    David H. Foos;W. James Sehnert;Bruce I. Reiner;Eliot L. Siegel

  • An Improved Index of Image Quality for Task-Based Performance of CT Iterative Reconstruction across Three Commercial Implementations | NIST

    Olav Christianson;Joseph Chen;Zhitong Yang;Ganesh Saiprasad

Frequent Co-Authors

Arman Rahmim
Arman Rahmim University of British Columbia
Ehsan Samei
Ehsan Samei Duke University
Elizabeth A. Krupinski
Elizabeth A. Krupinski Emory University
Christos Faloutsos
Christos Faloutsos Carnegie Mellon University
Anupam Joshi
Anupam Joshi University of Maryland, Baltimore County
Daniel L. Rubin
Daniel L. Rubin Stanford University
Hayit Greenspan
Hayit Greenspan Tel Aviv University
Metin N. Gurcan
Metin N. Gurcan Wake Forest University
Tulay Adali
Tulay Adali University of Maryland, Baltimore County

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