World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6283
World Ranking
10692
National Ranking
4470

Eliot L. Siegel publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Eliot L. Siegel sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 195 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Eliot L. Siegel D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Eliot L. Siegel sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

John A. Carrino
John A. Carrino Hospital for Special Surgery
Arman Rahmim
Arman Rahmim University of British Columbia
Ben F. Hurley
Ben F. Hurley University of Maryland, College Park
Jerome L. Fleg
Jerome L. Fleg National Institutes of Health
Ehsan Samei
Ehsan Samei Duke University
Elizabeth A. Krupinski
Elizabeth A. Krupinski Emory University
William F. Rosenberger
William F. Rosenberger George Mason University
Anupam Joshi
Anupam Joshi University of Maryland, Baltimore County
Daniel L. Rubin
Daniel L. Rubin Stanford University

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