Webster, Dale R.

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 7,697

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 EducationTopic Modeling

Cited by 4,083

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. (2022) · arXiv

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 4,083

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

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

Cited by 1,865

Toward expert-level medical question answering with large language models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 1,734

Direct detection of DNA methylation during single-molecule, real-time sequencing

Flusberg, B. A., Webster, D. R., Lee, J. H., Travers, K. J., Olivares, E. C., Clark, T. A., Korlach, J., & Turner, S. W. (2010) · Nature Methods

Epigenetics and DNA MethylationDNA and Nucleic Acid Chemistry

Cited by 1,460

Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy

Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 564

Towards Generalist Biomedical AI

Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 483

Towards Generalist Biomedical AI

Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2024) · NEJM AI

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 483

Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy

Sayres, R., Taly, A., Rahimy, E., Blumer, K., Coz, D., Hammel, N., Krause, J., Narayanaswamy, A., Rastegar, Z., Wu, D., et al. (2019) · Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 418

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

Cited by 311

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

Cited by 311

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

Cited by 303

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

Cited by 250

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

Cited by 233

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

Cited by 230

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

Retinal Imaging and AnalysisGlaucoma and retinal disorders

Cited by 186

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

Cited by 164

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

Cited by 139

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

Cited by 101

Pathologist validation of a machine learning–derived feature for colon cancer risk stratification

L’Imperio, V., Wulczyn, E., Plass, M., Müller, H., Tamini, N., Gianotti, L., Zucchini, N., Reihs, R., Corrado, G. S., Webster, D. R., et al. (2023) · JAMA Network Open

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 57

Remote Tool-Based Adjudication for Grading Diabetic Retinopathy

Schaekermann, M., Hammel, N., Terry, M., Ali, T. K., Liu, Y., Basham, B., Campana, B., Chen, W., Ji, X., Krause, J., et al. (2019) · Translational Vision Science & Technology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 19

Generative AI for medical education: Insights from a case study with medical students and an AI tutor for clinical reasoning

Wang, A., Ruparel, R., Iurchenko, A., Jhun, P., Séguin, J. A., Strachan, P., Wong, R., Karthikesalingam, A., Matias, Y., Hassidim, A., et al. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Education

Cited by 6