World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
125016
World Ranking
2374
National Ranking
1182

Greg Corrado 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 Greg Corrado 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: 139 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Greg Corrado 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 Greg Corrado 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: 65 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Overview

Greg Corrado is affiliated with Google in the United States and focuses on research at the intersection of medicine and computer science. Their work spans several areas within these broad fields, prominently featuring artificial intelligence and its applications in healthcare.

The scientist's main fields of study include:

  • Medicine
  • Computer Science

Within these fields, Corrado has contributed to several subfields such as:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Health Informatics
  • Oncology
  • Ophthalmology

The primary topics of Corrado's research work address:

  • Artificial Intelligence in Healthcare and Education
  • AI in cancer detection
  • Radiomics and Machine Learning in Medical Imaging
  • Machine Learning in Healthcare
  • Retinal Imaging and Analysis
  • COVID-19 diagnosis using AI
  • Topic Modeling

Corrado has published extensively, with notable papers including:

  • International evaluation of an AI system for breast cancer screening (2020, Nature)
  • Large language models encode clinical knowledge (2023, Nature)
  • Towards Expert-Level Medical Question Answering with Large Language Models (2023, arXiv (Cornell University))
  • Toward expert-level medical question answering with large language models (2025, Nature Medicine)
  • Towards Generalist Biomedical AI (2024, NEJM AI)

The frequent venues for Corrado's publications are:

  • arXiv (Cornell University)
  • Nature
  • Nature Medicine
  • JAMA Network Open
  • Nature Biomedical Engineering

Corrado frequently collaborates with a core group of coauthors, including:

  • Yun Liu
  • Yossi Matias
  • Dale R. Webster
  • Lily Peng
  • Shravya Shetty

Best Publications

  • Distributed Representations of Words and Phrases and their Compositionality

    Tomas Mikolov;Ilya Sutskever;Kai Chen;Greg S Corrado

  • Efficient Estimation of Word Representations in Vector Space

    Tomas Mikolov;Kai Chen;Greg S. Corrado;Jeffrey Dean

  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le

  • A guide to deep learning in healthcare.

    Andre Esteva;Alexandre Robicquet;Bharath Ramsundar;Volodymyr Kuleshov

  • Large Scale Distributed Deep Networks

    Jeffrey Dean;Greg Corrado;Rajat Monga;Kai Chen

  • Wide & Deep Learning for Recommender Systems

    Heng-Tze Cheng;Levent Koc;Jeremiah Harmsen;Tal Shaked

  • Building high-level features using large scale unsupervised learning

    Marc'aurelio Ranzato;Rajat Monga;Matthieu Devin;Kai Chen

  • DeViSE: A Deep Visual-Semantic Embedding Model

    Andrea Frome;Greg S Corrado;Jon Shlens;Samy Bengio

  • Scalable and accurate deep learning with electronic health records

    Alvin Rajkomar;Alvin Rajkomar;Eyal Oren;Kai Chen;Andrew M. Dai

  • Scalable and accurate deep learning for electronic health records

    Alvin Rajkomar;Eyal Oren;Kai Chen;Andrew M. Dai

  • Key challenges for delivering clinical impact with artificial intelligence.

    Christopher J. Kelly;Alan Karthikesalingam;Mustafa Suleyman;Greg Corrado

  • End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

    Diego Ardila;Atilla Peter Kiraly;Sujeeth Bharadwaj;Bokyung Choi

  • Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

    Melvin Johnson;Mike Schuster;Quoc V. Le;Maxim Krikun

  • Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

    Ryan Poplin;Avinash V Varadarajan;Katy Blumer;Yun Liu

  • Stimulus onset quenches neural variability: a widespread cortical phenomenon

    Mark M. Churchland;Byron M. Yu;Byron M. Yu;John P. Cunningham;Leo P. Sugrue;Leo P. Sugrue

  • Ensuring Fairness in Machine Learning to Advance Health Equity.

    Alvin Rajkomar;Michaela Hardt;Michael D Howell;Greg Corrado

  • Zero-Shot Learning by Convex Combination of Semantic Embeddings

    Mohammad Norouzi;Tomas Mikolov;Samy Bengio;Yoram Singer

  • Choosing the greater of two goods: neural currencies for valuation and decision making

    Leo P. Sugrue;Greg S. Corrado;William T. Newsome

  • Predicting Cardiovascular Risk Factors in Retinal Fundus Photographs using Deep Learning

    Ryan Poplin;Avinash V. Varadarajan;Katy Blumer;Yun Liu

Frequent Co-Authors

Jeffrey Dean
Jeffrey Dean Google (United States)
Kai Chen
Kai Chen Hong Kong University of Science and Technology
Quoc V. Le
Quoc V. Le Google (United States)
William T. Newsome
William T. Newsome Stanford University
Tomas Mikolov
Tomas Mikolov Czech Technical University in Prague
Fernanda B. Viégas
Fernanda B. Viégas Harvard University
Martin Wattenberg
Martin Wattenberg Harvard University
Marc'Aurelio Ranzato
Marc'Aurelio Ranzato DeepMind (United Kingdom)
Jonathon Shlens
Jonathon Shlens Google (United States)

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