Google Health research publications

Publishing our work allows us to share ideas and work collaboratively to advance healthcare. This is a comprehensive view of our publications and associated blog posts.

General

How you'll find accurate and timely information on COVID-19 vaccines

by Karen DeSalvo & Kristie Canegallo

Google Keyword Blog | 10-Dec-2020
How I’m giving thanks (and staying safe) this Thanksgiving

by Karen DeSalvo

Google Keyword Blog | 24-Nov-2020 [Spanish version]
A Q&A on coronavirus vaccines

Google Keyword Blog | 10-Nov-2020
An update on our efforts to help Americans navigate COVID-19

by Ruth Porat

Google Keyword Blog | 27-Oct-2020
Making data useful for public health

by Katherine Chou

Google Keyword Blog | 17-Sept-2020
Google supports COVID-19 AI and data analytics projects

by Mollie Javerbaum & Meghan Houghton

Google Keyword Blog | 10-Sep-2020
Using symptoms search trends to inform COVID-19 research

by Evgeniy Gabrilovich

Google Keyword Blog | 2-Sep-2020
An update on Exposure Notifications

by Dave Burke

Google Keyword Blog | 31-Jul-2020
Learn more about anxiety with a self-assessment on Search

by Daniel Gillison, Jr

Google Keyword Blog | 28-May-2020
Exposure Notification API launches to support public health agencies

by Apple & Google

Google Keyword Blog | 20-May-2020
Dr. Karen DeSalvo on ‘putting information first’ during COVID-19

by Megan Washam

Google Keyword Blog | 13-May-2020
Resources for mental health support during COVID-19

by David Feinberg

Google Keyword Blog | 8-May-2020
Apple and Google partner on COVID-19 contact tracing technology

by Apple & Google

Google Keyword Blog | 10-Apr-2020
Connecting people to virtual care options

by Julie Black

Google Keyword Blog | 10-Apr-2020
Support for public health workers fighting COVID-19

by Karen DeSalvo

Google Keyword Blog | 6-Apr-2020
Helping public health officials combat COVID-19

by Jen Fitzpatrick & Karen DeSalvo

Google Keyword Blog | 3-Apr-2020
Connecting people with COVID-19 information and resources

by Emily Moxley

Google Keyword Blog | 21-Mar-2020
COVID-19: How we’re continuing to help

by Sundar Pichai

Google Keyword Blog | 15-Mar-2020
Coronavirus: How we’re helping

by Sundar Pichai

Google Keyword Blog | 6-Mar-2020
A new Google Search tool to support women with postpartum depression

by David Feinberg

LinkedIn Blog | 8-Dec-2020
Advancing health research with Google Health Studies

by Jon Morgan & Paul Eastham

Google Keyword Blog | 9-Dec-2020
Prepare for medical visits with help from Google and AHRQ

by Dave Greenwood

Google Keyword Blog | 2-Dec-2020
Lessons Learned from Developing ML for Healthcare

by Yun Liu & Po-Hsuan Cameron Chen

Google AI Blog | 10-Dec-2019
Tools to help healthcare providers deliver better care

by David Feinberg

Google Keyword Blog | 20-Nov-2019
Breast cancer and tech...a reason for optimism

by Ruth Porat

Google Keyword Blog | 21-Oct-2019
DeepMind’s health team joins Google Health

by Dominic King

Google Keyword Blog | 18-Sep-2019
Meet David Feinberg, head of Google Health

by Google

Google Keyword Blog | 17-Jun-2019
AI for Social Good in Asia Pacific

by Kent Walter

Google Keyword Blog | 13-Dec-2018
Gain a deeper understanding of Posttraumatic Stress Disorder on Google

by Paula Schnurr & Teri Brister

Google Keyword Blog | 5-Dec-2017
Learning more about clinical depression with the PHQ-9 questionnaire

by Mary Giliberti

Google Keyword Blog | 23-Aug-2017
Partnering on machine learning in healthcare

by Katherine Chou

Google AI Blog | 17-May-2017
Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic retinopathy from retinal fundus photographs

Liu, Y., Yang, L., Phene, S. & Peng, L.

Artificial Intelligence in Medicine 247–264 (2021). doi:10.1016/b978-0-12-821259-2.00013-2
Current and future applications of artificial intelligence in pathology: a clinical perspective

Rakha, E. A., Toss, M., Shiino, S., Gamble, P., Jaroensri, R., Mermel, C. H. & Chen, P.-H. C.

J. Clin. Pathol. (2020). doi:10.1136/jclinpath-2020-206908
Artificial intelligence, machine learning and deep learning for eye care specialists

Sayres, R., Hammel, N. & Liu, Y.

Annals of Eye Science 5, 18–18 (2020).
Artificial intelligence in digital breast pathology: Techniques and applications

Ibrahim, A., Gamble, P., Jaroensri, R., Abdelsamea, M. M., Mermel, C. H., Chen, P.-H. C. & Rakha, E. A.

Breast 49, 267–273 (2020).
How to Read Articles That Use Machine Learning: Users’ Guides to the Medical Literature

Liu, Y., Chen, P.-H. C., Krause, J. & Peng, L.

JAMA 322, 1806–1816 (2019).
Key challenges for delivering clinical impact with artificial intelligence

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D.

BMC Med. 17, 195 (2019).
Artificial Intelligence Approach in Melanoma

Curiel-Lewandrowski, C., Novoa, R. A., Berry, E., Celebi, M. E., Codella, N., Giuste, F., Gutman, D., Halpern, A., Leachman, S., Liu, Y., Liu, Y., Reiter, O. & Tschandl, P.

599–628 (Springer New York, 2019).
How to develop machine learning models for healthcare.

Chen, C. P.-H., Liu, Y., & Peng, L.

Nat. Mater. 18, 410–414 (2019). [readcube]
Machine Learning in Medicine

Rajkomar, A., Dean, J., & Kohane I.

N. Engl. J. Med. 380:1347-1358 (2019).
A guide to deep learning in healthcare.

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S. & Dean, J.

Nat. Med. 25, 24–29 (2019). [readcube]
Ensuring Fairness in Machine Learning to Advance Health Equity

Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H.

Ann. Intern. Med. 169(12):866-872 (2018).
When does size matter? -- Promises, pitfalls, and appropriate interpretations of ‘big’ data

Rough K, Thompson J.

Ophthalmology. 125(8):1136-1138 (2018).
Resolving the Productivity Paradox of Health Information Technology: A Time for Optimism

Wachter, R. M., Howell, M. D.

JAMA 320(1):25-26 (2018).

Dermatology

Generating Diverse Synthetic Medical Image Data for Training Machine Learning Models

by Timo Kohlberger & Yuan Liu

Google AI Blog | 19-Feb-2020
Using Deep Learning to Inform Differential Diagnoses of Skin Diseases

by Yuan Liu & Peggy Bui

Google AI Blog | 12-Sep-2019
Addressing the Real-world Class Imbalance Problem in Dermatology

Weng, W.-H., Deaton, J., Natarajan, V., Elsayed, G. F. & Liu, Y.

arXiv [cs.CV] (2020). at http://arxiv.org/abs/2010.04308
Agreement Between Saliency Maps and Human-Labeled Regions of Interest: Applications to Skin Disease Classification

Singh, N., Lee, K., Coz, D., Angermueller, C., Huang, S., Loh, A. & Liu, Y.

in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 3172–3181 (2020).
A deep learning system for differential diagnosis of skin diseases

Liu, Y., Jain, A., Eng, C., Way, D. H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S., Gupta, V., Singh, N., Natarajan, V., Hofmann-Wellenhof, R., Corrado, G. S., Peng, L. H., Webster, D. R., Ai, D., Huang, S., Liu, Y., Carter Dunn, R. & Coz, D.

Nat. Med. (2020). [readcube]
DermGAN: Synthetic Generation of Clinical Skin Images with Pathology

Ghorbani, A., Natarajan, V., Coz, D. & Liu, Y.

arXiv [cs.CV] (2019).
Measuring clinician-machine agreement in differential diagnoses for dermatology

Eng, C., Liu, Y. & Bhatnagar, R.

Br. J. Dermatol. (2019).

Endoscopy

Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screenings

by Daniel Freedman & Ehud Rivlin

Google AI Blog | 28-Aug-2020
Detecting Deficient Coverage in Colonoscopies

Freedman, D., Blau, Y., Katzir, L., Aides, A., Shimshoni, I., Veikherman, D., Golany, T., Gordon, A., Corrado, G., Matias, Y. & Rivlin, E.

IEEE Trans. Med. Imaging 1–1 (2020).

Eye Diseases

Healthcare AI systems that put people at the center

by Emma Beede

Google Keyword Blog | 25-Apr-2020
New milestones in helping prevent eye disease with Verily

by Kasumi Widner & Sunny Virmani

Google Keyword Blog | 25-Feb-2019
Launching a powerful new screening tool for diabetic eye disease in India

Verily Blog | 25-Feb-2019
Improving the Effectiveness of Diabetic Retinopathy Models

by Rory Sayres & Jonathan Krause

Google AI Blog | 13-Dec-2018
A major milestone for the treatment of eye disease

by Mustafa Suleyman

DeepMind Blog | 13-Aug-2018
Detecting diabetic eye disease with machine learning

by Lily Peng

Google Keyword Blog | 29-Nov-2016
Deep learning for Detection of Diabetic Eye Disease

by Lily Peng & Varun Gulshan

Google AI Blog | 29-Nov-2016
Improving medical annotation quality to decrease labeling burden using stratified noisy cross-validation

Hsu J, Phene S, Mitani A, Luo J, Hammel N, Krause J, Sayres R.

in ACM-CHIL [arXiv](2020)
Adherence to ophthalmology referral, treatment and follow-up after diabetic retinopathy screening in the primary care setting

Bresnick, G., Cuadros, J. A., Khan, M., Fleischmann, S., Wolff, G., Limon, A., Chang, J., Jiang, L., Cuadros, P. & Pedersen, E. R.

BMJ Open Diabetes Research and Care 8, e001154 (2020).
A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Beede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P. & Vardoulakis, L. M.

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–12 (Association for Computing Machinery, 2020).
Expert Discussions Improve Comprehension of Difficult Cases in Medical Image Assessment

Schaekermann, M., Cai, C. J., Huang, A. E. & Sayres, R.

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–13 (Association for Computing Machinery, 2020).
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs

Phene, S. et al.

Ophthalmology 126, 1627–1639 (2019).
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., Corrado, G. S., Peng, L., Webster, D. R., Law, E. & Sayres, R.

Transl. Vis. Sci. Technol. 8, 40 (2019).
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., Raman, R., Peng, L. & Webster, D. R.

JAMA Ophthalmol. (2019).
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., Silpa-Archa, S., Limwattanayingyong, J., Rao, C., Kuruvilla, O., Jung, J., Tan, J., Orprayoon, S., Kangwanwongpaisan, C., Sukumalpaiboon, R., Luengchaichawang, C., Fuangkaew, J., Kongsap, P., Chualinpha, L., Saree, S., Kawinpanitan, S., Mitvongsa, K., Lawanasakol, S., Thepchatri, C., Wongpichedchai, L., Corrado, G. S., Peng, L. & Webster, D. R.

npj Digit Med 2, 25 (2019).
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., Xu, S., Barb, S., Joseph, A., Shumski, M., Smith, J., Sood, A. B., Corrado, G. S., Peng, L. & Webster, D. R.

Ophthalmology 126, 552–564 (2019).
Clinically applicable deep learning for diagnosis and referral in retinal disease

Fauw, J., Ledsam, J.R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., van den Driessche, G., Lakshminarayanan, B., Meyer, C., Mackinder, F., Bouton, S., Ayoub, K., Chopra, R., King, D., Karthikesalingam, A., Hughes, C.O., Raine, R., Hughes, J., Sim, D. A., Egan, C., Tufail, A., Montgomery, H., Hassabis, D., Rees, G., Back, T., Khaw, P.T., Suleyman, M., Cornebise, J., Keane, P.A., & Ronneberger, O.

Nat. Med. 24, 1342–1350 (2018). [readcube]
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.

Ophthalmology 125, 1264–1272 (2018).
Blind spots in telemedicine: a qualitative study of staff workarounds to resolve gaps in diabetes management

Bouskill, K., Smith-Morris, C., Bresnick, G., Cuadros, J. & Pedersen, E. R.

BMC Health Services Research 18, (2018).
Diabetic Retinopathy and the Cascade into Vision Loss

Smith-Morris, C., Bresnick, G. H., Cuadros, J., Bouskill, K. E. & Pedersen, E. R.

Med. Anthropol. 39, 109–122 (2018).
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., Ramasamy, K., Nelson, P., Mega, J., & Webster, D.

JAMA 316, 2402–2410 (2016).

Genomics

Improving the Accuracy of Genomic Analysis with DeepVariant 1.0

by Andrew Carroll & Pi-Chuan Chang

Google AI Blog | 18-Sep-2020
DeepVariant Accuracy Improvements for Genetic Datatypes

by Pi-Chuan Chang & Lizzie Dorfman

Google AI Blog | 19-Apr-2018
DeepVariant: Highly Accurate Genomes With Deep Neural Networks

by Mark DePristo & Ryan Poplin

Google AI Blog | 4-Dec-2017
An AI Resident at work: Suhani Vora and her work on genomics

by Phing Lee

Google Keyword Blog | 17-Nov-2017
GenomeWarp: an alignment-based variant coordinate transformation

McLean, C. Y., Hwang, Y., Poplin, R. & DePristo, M. A.

Bioinformatics 35, 4389–4391 (2019).
Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome

Wenger, A. M., Peluso, P., Rowell, W. J., Chang, P.-C., Hall, R. J., Concepcion, G. T., Ebler, J., Fungtammasan, A., Kolesnikov, A., Olson, N. D., Töpfer, A., Alonge, M., Mahmoud, M., Qian, Y., Chin, C.-S., Phillippy, A. M., Schatz, M. C., Myers, G., DePristo, M. A., Ruan, J., Marschall, T., Sedlazeck, F. J., Zook, J. M., Li, H., Koren, S., Carroll, A., Rank, D. R. & Hunkapiller, M. W.

Nat. Biotechnol. 37, 1155–1162 (2019). [readcube]
A universal SNP and small-indel variant caller using deep neural networks

Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., Gross, S. S., Dorfman, L., McLean, C. Y. & DePristo, M. A.

Nat. Biotechnol. 36, 983–987 (2018). [readcube]
Deep learning of genomic variation and regulatory network data

Telenti, A., Lippert, C., Chang, P.-C. & DePristo, M.

Hum. Mol. Genet. 27, R63–R71 (2018).
Sequential regulatory activity prediction across chromosomes with convolutional neural networks

Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y. & Snoek, J.

Genome Res. 28, 739–750 (2018).

Medical Audio

Joint Speech Recognition and Speaker Diarization via Sequence Transduction

by Laurent El Shafey and Izhak Shafran

16-Aug-2019
How AI can improve products for people with impaired speech

by Julie Cattiau

Google Keyword Blog | 7-May-2019
Understanding Medical Conversations

by Katherine Chou and Chung-Cheng Chiu

Google AI Blog | 21-Nov-2017
Medical Scribe: Corpus Development and Model Performance Analyses

Shafran, I., Du, N., Tran, L., Perry, A., Keyes, L., Knichel, M., Domin, A., Huang, L., Chen, Y., Li, G., Wang, M., El Shafey, L., Soltau, H. & Paul, J. S.

Proceedings of the Language Resources and Evaluation Conference. arXiv [cs.CL] (2020).
Extracting Symptoms and their Status from Clinical Conversations

Du, N., Chen, K., Kannan, A., Tran, L., Chen, Y. & Shafran, I.

Proceedings of the Annual Meeting of the Association of Computational Linguistics. arXiv [cs.LG] (2019). at http://arxiv.org/abs/1912.01762
Automatically Charting Symptoms From Patient-Physician Conversations Using Machine Learning

Rajkomar, A., Kannan, A., Chen, K., Vardoulakis, L., Chou, K., Cui, C., & Dean, J.

JAMA Intern. Med. 179, 836–838 (2019).
Joint Speech Recognition and Speaker Diarization via Sequence Transduction

El Shafey, L., Soltau, H. & Shafran, I.

Proceedings of Interspeech. arXiv [cs.CL] (2019). at http://arxiv.org/abs/1907.05337
Learning to Infer Entities, Properties and their Relations from Clinical Conversations

Du, N., Wang, M., Tran, L., Li, G. & Shafran, I.

Proc. Empirical Methods in Natural Language Processing. arXiv [cs.CL] (2019).
Speech recognition for medical conversations

Chiu, C.-C., Tripathi, A., Chou, K., Co, C., Jaitly, N., Jaunzeikare, D., Kannan, A., Nguyen, P., Sak, H., Sankar, A., Tansuwan, J., Wan, N., Wu, Y., & Zhang X.

arXiv [cs.CL] (2017). at http://arxiv.org/abs/1711.07274

Medical Records

A Step Towards Protecting Patients from Medication Errors

by Kathryn Rough & Alvin Rajkomar

Google AI Blog | 2-Apr-2020
Expanding the Application of Deep Learning to Electronic Health Records

by Alvin Rajkomar & Eyal Oren

Google AI Blog | 22-Jan-2019
Scaling Streams with Google

by Demis Hassabis & Mustafa Suleyman & Dominic King

DeepMind Blog | 13-Nov-2018
Deep Learning for Electronic Health Records

by Alvin Rajkomar & Eyal Oren

Google AI Blog | 8-May-2018
Making Healthcare Data Work Better with Machine Learning

by Patrik Sundberg & Eyal Oren

Google AI Blog | 2-Mar-2018
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction

Xue Y, Du N, Mottram A, Seneviratne A, Dai AM.

NeurIPS (2020).
Deep State-Space Generative Model For Correlated Time-to-Event Predictions

Xue Y, Zhou D, Du N, Dai A, Xu Z, Zhang K, Cui C.

KDD (2020).
Graph convolutional transformer: Learning the graphical structure of electronic health records

Choi E, Xu Z, Li Y, Dusenberry MW, Flores G, Xue Y, Dai AM.

AAAI (2020).
Analyzing the role of model uncertainty for electronic health records

Dusenberry MW, Tran D, Choi E, Kemp J, Nixon J, Jerfel G, Heller K, & Dai AM.

ACM CHIL (2020).
Explaining an increase in predicted risk for clinical alerts

Hardt M, Rajkomar A, Flores G, Dai A, Howell M, Corrado G, Cui C, & Hardt M.

ACM CHIL (2020).
Predicting inpatient medication orders from electronic health record data

Rough, K., Dai, A. M., Zhang, K., Xue, Y., Vardoulakis, L. M., Cui, C., Butte, A. J., Howell, M. D. & Rajkomar, A.

Clin. Pharmacol. Ther. 108, 145–154 (2020).
A clinically applicable approach to continuous prediction of future acute kidney injury

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Peterson, K., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Ledsam, J. R. & Mohamed, S.

Nature 572, 116–119 (2019). [readcube]
Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Peterson, K., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Ledsam, J. R. & Mohamed, S.

Protocol Exchange (2019). doi:10.21203/rs.2.10083/v1
Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissions

Connell, A., Montgomery, H., Martin, P., Nightingale, C., Sadeghi-Alavijeh, O., King, D., Karthikesalingam, A., Hughes, C., Back, T., Ayoub, K., Suleyman, M., Jones, G., Cross, J., Stanley, S., Emerson, M., Merrick, C., Rees, G., Laing, C. & Raine, R.

npj Digit Med 2, 67 (2019).
Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs

Connell A., Raine R., Martin P., Barbosa E.C., Morris S., Nightingale C., Sadeghi-Alavijeh O., King D., Karthikesalingam A., Hughes C., Back T., Ayoub K., Suleyman M., Jones G., Cross J., Stanley S., Emerson M., Merrick C., Rees G., Montgomery H., & Laing C.

J Med Internet Res 21(7):e13147 (2019).
Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals

Connell A, Black G, Montgomery H, Martin P, Nightingale C, King D, Karthikesalingam A, Hughes C, Back T, Ayoub K, Suleyman M, Jones G, Cross J, Stanley S, Emerson M, Merrick C, Rees G, Laing C, & Raine R.

J Med Internet Res 21(7):e13143 (2019).
Improved Patient Classification with Language Model Pretraining Over Clinical Notes

Kemp J, Rajkomar A, & Dai AM.

arXiv [cs.LG] (2019). at http://arxiv.org/abs/1909.03039
Federated and Differentially Private Learning for Electronic Health Records

Pfohl SR, Dai AM, & Heller K.

arXiv [cs.LG] (2019). at http://arxiv.org/abs/1911.05861
Deep Physiological State Space Model for Clinical Forecasting

Xue Y, Zhou D, Du N, Dai AM, Xu Z, Zhang K,& Cui C.

arXiv [cs.LG] (2019). at http://arxiv.org/abs/1912.01762
Modelling EHR timeseries by restricting feature interaction

Zhang K, Xue Y, Flores G, Rajkomar A, Cui C, & Dai AM.

arXiv [cs.LG] (2019). at http://arxiv.org/abs/1911.06410
Scalable and accurate deep learning with electronic health records

Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte AJ, Howell MD, Cui C, Corrado GS, Dean J.

npj Digital Med 1, 18 (2018).

Novel Biomarkers

How AI could predict sight-threatening eye conditions

by Terry Spitz & Jim Winkens

Google Keyword Blog | 18-May-2020
Using AI to predict retinal disease progression

by Jason Yim, Reena Chopra, Jeffrey De Fauw & Joseph Ledsam

DeepMind Blog | 18-May-2020
Detecting hidden signs of anemia from the eye

by Akinori Mitani

Google Keyword Blog | 28-Jan-2020
Assessing Cardiovascular Risk Factors with Computer Vision

by Lily Peng

Google AI Blog | 2-Feb-2018
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., Peng, L., Webster, D. R., Varadarajan, A. V., Hammel, N., Liu, Y. & Bavishi, P.

The Lancet Digital Health (2020). doi:10.1016/S2589-7500(20)30250-8
Underspecification Presents Challenges for Credibility in Modern Machine Learning

D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., Hormozdiari, F., Houlsby, N., Hou, S., Jerfel, G., Karthikesalingam, A., Lucic, M., Ma, Y., McLean, C., Mincu, D., Mitani, A., Montanari, A., Nado, Z., Natarajan, V., Nielson, C., Osborne, T. F., Raman, R., Ramasamy, K., Sayres, R., Schrouff, J., Seneviratne, M., Sequeira, S., Suresh, H., Veitch, V., Vladymyrov, M., Wang, X., Webster, K., Yadlowsky, S., Yun, T., Zhai, X. & Sculley, D.

arXiv [cs.LG] (2020).
Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning

Moraes, G., Fu, D. J., Wilson, M., Khalid, H., Wagner, S. K., Korot, E., Ferraz, D., Faes, L., Kelly, C. J., Spitz, T., Patel, P. J., Balaskas, K., Keenan, T. D. L., Keane, P. A. & Chopra, R.

Ophthalmology (2020). doi:10.1016/j.ophtha.2020.09.025
Scientific Discovery by Generating Counterfactuals Using Image Translation

Narayanaswamy, A., Venugopalan, S., Webster, D. R., Peng, L., Corrado, G. S., Ruamviboonsuk, P., Bavishi, P., Brenner, M., Nelson, P. C. & Varadarajan, A. V.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 273–283 (2020). doi:10.1007/978-3-030-59710-8_27 arXiv
Predicting conversion to wet age-related macular degeneration using deep learning

Yim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., Moraes, G., Meyer, C., Wilson, M., Dixon, J., Hughes, C., Rees, G., Khaw, P. T., Karthikesalingam, A., King, D., Hassabis, D., Suleyman, M., Back, T., Ledsam, J. R., Keane, P. A. & De Fauw, J.

Nat. Med. (2020). [readcube]
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., Silpa-Archa, S., Limwattanayingyong, J., Nganthavee, V., Ledsam, J. R., Keane, P. A., Corrado, G. S., Peng, L. & Webster, D. R.

Nat. Commun. 11, 130 (2020).
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.

Nat Biomed Eng (2019). [readcube]
Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning

Babenko, B., Balasubramanian, S., Blumer, K. E., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N. & Varadarajan, A. V.

arXiv [cs.CV] (2019). at http://arxiv.org/abs/1904.05478
Deep Learning for Predicting Refractive Error From Retinal Fundus Images

aradarajan, A.V., Poplin, R., Blumer, K., Angermueller, C., Lesdam, J., Chopra, R., Keane, P.A., Corrado, G. S., Peng, L., Webster, D. R.

Invest. Ophthalmol. Vis. Sci. 59, 2861–2868 (2018).
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.

Nat. Biomed. Eng. 2, 158–164 (2018). [readcube]

Pathology

Using AI to identify the aggressiveness of prostate cancer

by Kunal Nagpal & Craig Mermel

Google Keyword Blog | 23-Jul-2020
Generating Diverse Synthetic Medical Image Data for Training Machine Learning Models

by Timo Kohlberger & Yuan Liu

Google AI Blog | 19-Feb-2020
Building SMILY, a Human-Centric, Similar-Image Search Tool for Pathology

by Narayan Hedge & Carrie Cai

Google AI Blog | 19-July-2019
Improved Grading of Prostate Cancer Using Deep Learning

by Martin Stumpe & Craig Mermel

Google AI Blog | 16-Nov-2018
Applying Deep Learning to Metastatic Breast Cancer Detection

by Martin Stumpe & Craig Mermel

Google AI Blog | 12-Oct-2018
An Augmented Reality Microscope for Cancer Detection

by Martin Stumpe & Craig Mermel

Google AI Blog | 16-Apr-2018
Assisting Pathologists in Detecting Cancer with Deep Learning

by Martin Stumpe & Lily Peng

Google AI Blog | 3-Mar-2017
Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies

Steiner, D. F., Nagpal, K., Sayres, R., Foote, D. J., Wedin, B. D., Pearce, A., Cai, C. J., Winter, S. R., Symonds, M., Yatziv, L., Kapishnikov, A., Brown, T., Flament-Auvigne, I., Tan, F., Stumpe, M. C., Jiang, P.-P., Liu, Y., Chen, P.-H. C., Corrado, G. S., Terry, M. & Mermel, C. H.

JAMA Netw Open 3, e2023267–e2023267 (2020).
Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

Nagpal, K., Foote, D., Tan, F., Liu, Y., Chen, P.-H. C., Steiner, D. F., Manoj, N., Olson, N., Smith, J. L., Mohtashamian, A., Peterson, B., Amin, M. B., Evans, A. J., Sweet, J. W., Cheung, C., van der Kwast, T., Sangoi, A. R., Zhou, M., Allan, R., Humphrey, P. A., Hipp, J. D., Gadepalli, K., Corrado, G. S., Peng, L. H., Stumpe, M. C. & Mermel, C. H.

JAMA Oncol (2020).
Deep learning-based survival prediction for multiple cancer types using histopathology images

Wulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., Mermel, C. H., Chen, P.-H. C., Liu, Y. & Stumpe, M. C.

PLOS ONE 15, e0233678 (2020).
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

Kohlberger, T., Liu, Y., Moran, M., Chen, P.-H. C., Brown, T., Hipp, J. D., Mermel, C. H. & Stumpe, M. C.

J. Pathol. Inform. 10, 39 (2019).
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Chen, P.C., Gadepalli, K., MacDonald, R., Liu, Y., Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G.S., Hipp, J.D., Mermel, C.H., Stumpe, M. C.

Nat Med 25, 1453–1457 (2019). [readcube]
Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N., Peng, L.H., Hipp, J.D., Stumpe, M.C. (2019).

Arch. Pathol. Lab. Med. 143, 859–868 (2019).
Similar image search for histopathology: SMILY

Hegde, N., Hipp, J. D., Liu, Y., Emmert-Buck, M., Reif, E., Smilkov, D., Terry, M., Cai, C. J., Amin, M. B., Mermel, C. H., Nelson, P. Q., Peng, L. H., Corrado, G. S. & Stumpe, M. C.

npj Digit Med 2, 56 (2019).
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Nagpal, K., Foote, D., Liu, Y., Chen, P.H.C., Wulczyn, E., Tan, F., Olson, N., Smith, J.L., Mohtashamian, A., Wren, J.H., Corrado, G.S., MacDonald, R., Peng, L. H., Amin, M.B., Evans, A.J., Sanjoi, A.R., Mermel, C. H., Hipp, J. D., Stumpe, M. C.

npj Digit. Med. 2, 48 (2019).
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer

Steiner, D. F., MacDonald, R., Liu, Y., Truszkowski, P., Hipp, J. D., Gammage, C., Thng, F., Peng, L., Stumpe, M.C.

Am. J. Surg. Pathol. 42, 1636–1646 (2018).
Detecting cancer metastases on gigapixel pathology images

Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G.E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P.Q., Corrado, G.S. and Hipp, J.D., Peng, L., Stumpe, M. C.

arXiv preprint arXiv:1703.02442, 2017

Public & Environmental Health

New Insights into Human Mobility with Privacy Preserving Aggregation

by Adam Sadilek & Xerxes Dotiwalla

Google AI Blog | 12-Nov-2019
Global maps of travel time to healthcare facilities

Weiss, D. J., Nelson, A., Vargas-Ruiz, C. A., Gligorić, K., Bavadekar, S., Gabrilovich, E., Bertozzi-Villa, A., Rozier, J., Gibson, H. S., Shekel, T., Kamath, C., Lieber, A., Schulman, K., Shao, Y., Qarkaxhija, V., Nandi, A. K., Keddie, S. H., Rumisha, S., Amratia, P., Arambepola, R., Chestnutt, E. G., Millar, J. J., Symons, T. L., Cameron, E., Battle, K. E., Bhatt, S. & Gething, P. W.

Nat. Med. (2020).
Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state

Abueg, M., Hinch, R., Wu, N., Liu, L., Probert, W. J. M., Wu, A., Eastham, P., Shafi, Y., Rosencrantz, M., Dikovsky, M., Cheng, Z., Nurtay, A., Abeler-Dörner, L., Bonsall, D. G., McConnell, M. V., O’Banion, S. & Fraser, C.

medRxiv (2020). doi:10.1101/2020.08.29.20184135
Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)

Bavadekar, S., Dai, A., Davis, J., Desfontaines, D., Eckstein, I., Everett, K., Fabrikant, A., Flores, G., Gabrilovich, E., Gadepalli, K., Glass, S., Huang, R., Kamath, C., Kraft, D., Kumok, A., Marfatia, H., Mayer, Y., Miller, B., Pearce, A., Perera, I. M., Ramachandran, V., Raman, K., Roessler, T., Shafran, I., Shekel, T., Stanton, C., Stimes, J., Sun, M., Wellenius, G. & Zoghi, M.

arXiv [cs.CR] (2020). at http://arxiv.org/abs/2009.01265
Impacts of State-Level Policies on Social Distancing in the United States Using Aggregated Mobility Data during the COVID-19 Pandemic

Wellenius, G. A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T. C., Hennessy, J., Williams, B., Gadepalli, K., Boulanger, A., Pearce, A., Kamath, C., Schlosberg, A., Bendebury, C., Stanton, C., Bavadekar, S., Pluntke, C., Desfontaines, D., Jacobson, B., Armstrong, Z., Gipson, B., Wilson, R., Widdowson, A., Chou, K., Oplinger, A., Shekel, T., Jha, A. K. & Gabrilovich, E.

arXiv [q-bio.PE] (2020). at http://arxiv.org/abs/2004.10172
Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.0)

Aktay, A., Bavadekar, S., Cossoul, G., Davis, J., Desfontaines, D., Fabrikant, A., Gabrilovich, E., Gadepalli, K., Gipson, B., Guevara, M., Kamath, C., Kansal, M., Lange, A., Mandayam, C., Oplinger, A., Pluntke, C., Roessler, T., Schlosberg, A., Shekel, T., Vispute, S., Vu, M., Wellenius, G., Williams, B. & Wilson, R. J.

arXiv [cs.CR] (2020). at http://arxiv.org/abs/2004.04145
Lymelight: forecasting Lyme disease risk using web search data

Sadilek, A., Hswen, Y., Bavadekar, S., Shekel, T., Brownstein, J. S. & Gabrilovich, E.

npj Digital Medicine 3, 1–12 (2020).
Hierarchical organization of urban mobility and its connection with city livability

Bassolas, A., Barbosa-Filho, H., Dickinson, B., Dotiwalla, X., Eastham, P., Gallotti, R., Ghoshal, G., Gipson, B., Hazarie, S. A., Kautz, H., Kucuktunc, O., Lieber, A., Sadilek, A., & Ramasco, J. J.

Nat. Commun. 10, 4817 (2019).
Machine-learned epidemiology: real-time detection of foodborne illness at scale

Sadilek, A., Caty, S., DiPrete, L., Mansour, R., Schenk Jr., T., Bergtholdt, M., Jha, A., Ramaswami P., & Gabrilovich E.

npj Digital Med 1, 36 (2018).

Radiology

Exploring AI for radiotherapy planning with Mayo Clinic

by Cian Hughes

Google Keyword Blog | 29-Oct-2020
Using AI to improve breast cancer screening

by Shravya Shetty & Daniel Tse

Google Keyword Blog | 1-Jan-2020
Developing Deep Learning Models for Chest X-rays with Adjudicated Image Labels

by Dave Steiner & Shravya Shetty

Google AI Blog | 3-Dec-2019
A promising step forward for predicting lung cancer

by Shravya Shetty

Google Keyword Blog | 20-May-2019
International evaluation of an AI system for breast cancer screening

McKinney, S.M., Sieniek, M., Godbole, V. et al.

Nature 577, 89–94 (2020).[readcube]
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., Ding, A., Corrado, G. S., Tse, D. & Shetty, S.

Radiology 191293 (2019).
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reciher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., Naidich, D. P., Shetty, S.

Nat. Med. 25, 954–961 (2019). [readcube]
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Nikolov, S., Blackwell, S., Zverovitch, A., Mendes, R., Livne, M., De Fauw, J., Patel, Y., Meyer, C., Askham, H. Romera-Paredes, Karthikesalingam, A., Chu, C., Carnell, D., Boon, C., D’Souza, D., Moinuddin, S. A., DeepMind Radiographer Consortium, Montgomery, H., Rees, G., Suleyman, M., Back, T., Hughes, C., Ledsam, J. R., & Ronneberger, O.

arXiv [cs.CV] (2018). at http://arxiv.org/abs/1809.04430