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Computer Science

D-Index
65
Citations
125016
World Ranking
2374
National Ranking
1181

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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