Corrado, Greg S.

Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

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Towards conversational diagnostic artificial intelligence

Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Cheng, Y., et al. (2025) · Nature

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

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An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Chen, P.-H. C., Gadepalli, K., MacDonald, R., Liu, Y., Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G. S., Hipp, J. D., et al. (2019) · Nature Medicine

AI in cancer detectionAugmented Reality Applications

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Towards Expert-Level Medical Question Answering with Large Language Models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationTopic Modeling

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Towards accurate differential diagnosis with large language models

McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., et al. (2025) · Nature

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

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Towards Accurate Differential Diagnosis with Large Language Models

McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

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Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

Gulshan, V., Rajan, R. P., Widner, K., Wu, D., Wubbels, P., Rhodes, T., Whitehouse, K., Coram, M., Corrado, G., Ramasamy, K., et al. (2019) · JAMA Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

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Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation

Majkowska, A., Mittal, S., Steiner, D. F., Reicher, J. J., McKinney, S. M., Duggan, G. E., Eswaran, K., Cameron Chen, P.-H., Liu, Y., Kalidindi, S. R., et al. (2020) · Radiology

COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

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Predicting the risk of developing diabetic retinopathy using deep learning

Bora, A., Balasubramanian, S., Babenko, B., Virmani, S., Venugopalan, S., Mitani, A., de Oliveira Marinho, G., Cuadros, J., Ruamviboonsuk, P., Corrado, G. S., et al. (2021) · The Lancet Digital Health

Retinal Imaging and AnalysisRetinal Diseases and Treatments

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Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

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Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program

Ruamviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., Campana, B. J. L., Phene, S., Hemarat, K., Tadarati, M., et al. (2019) · npj Digital Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

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Interpretable survival prediction for colorectal cancer using deep learning

Wulczyn, E., Steiner, D. F., Moran, M., Plass, M., Reihs, R., Tan, F., Flament-Auvigne, I., Brown, T., Regitnig, P., Chen, P.-H. C., et al. (2021) · npj Digital Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

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Detection of anaemia from retinal fundus images via deep learning

Mitani, A., Huang, A., Venugopalan, S., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Liu, Y., & Varadarajan, A. V. (2019) · Nature Biomedical Engineering

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

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LINEAR-NONLINEAR-POISSON MODELS OF PRIMATE CHOICE DYNAMICS

Corrado, G. S., Sugrue, L. P., Seung, H. S., & Newsome, W. T. (2005) · Journal of the Experimental Analysis of Behavior

Neural and Behavioral Psychology StudiesDecision-Making and Behavioral Economics

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Deep Learning and Glaucoma Specialists

Phene, S., Dunn, R. C., Hammel, N., Liu, Y., Krause, J., Kitade, N., Schaekermann, M., Sayres, R., Wu, D. J., Bora, A., et al. (2019) · Ophthalmology

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Three Epochs of Artificial Intelligence in Health Care

Howell, M. D., Corrado, G. S., & DeSalvo, K. B. (2024) · JAMA

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Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices

Jain, A., Way, D., Gupta, V., Gao, Y., de Oliveira Marinho, G., Hartford, J., Sayres, R., Kanada, K., Eng, C., Nagpal, K., et al. (2021) · JAMA Network Open

Artificial Intelligence in Healthcare and EducationCutaneous Melanoma Detection and Management

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Determining breast cancer biomarker status and associated morphological features using deep learning

Gamble, P., Jaroensri, R., Wang, H., Tan, F., Moran, M., Brown, T., Flament-Auvigne, I., Rakha, E. A., Toss, M., Dabbs, D. J., et al. (2021) · Communications Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

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Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Varadarajan, A. V., Bavishi, P., Ruamviboonsuk, P., Chotcomwongse, P., Venugopalan, S., Narayanaswamy, A., Cuadros, J., Kanai, K., Bresnick, G., Tadarati, M., et al. (2020) · Nature Communications

Retinal Imaging and AnalysisRetinal Diseases and Treatments

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Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

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A toolbox for surfacing health equity harms and biases in large language models

Pfohl, S. R., Cole-Lewis, H., Sayres, R., Neal, D., Asiedu, M., Dieng, A., Tomasev, N., Rashid, Q. M., Azizi, S., Rostamzadeh, N., et al. (2024) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

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Publisher Correction: Large language models encode clinical knowledge

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

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Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditions

Guha Roy, A., Ren, J., Azizi, S., Loh, A., Natarajan, V., Mustafa, B., Pawlowski, N., Freyberg, J., Liu, Y., Beaver, Z., et al. (2022) · Medical Image Analysis

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The Algorithmic Automation Problem: Prediction, Triage, and Human Effort

Raghu, M., Blumer, K., Corrado, G., Kleinberg, J., Obermeyer, Z., & Mullainathan, S. (2019) · arXiv

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

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