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
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How you'll find accurate and timely information on COVID-19 vaccines
by Karen DeSalvo & Kristie Canegallo
Google Keyword Blog | 10-Dec-2020
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How I’m giving thanks (and staying safe) this Thanksgiving
by Karen DeSalvo
Google Keyword Blog | 24-Nov-2020 [Spanish version]
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A Q&A on coronavirus vaccines
Google Keyword Blog | 10-Nov-2020
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An update on our efforts to help Americans navigate COVID-19
by Ruth Porat
Google Keyword Blog | 27-Oct-2020
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Making data useful for public health
by Katherine Chou
Google Keyword Blog | 17-Sept-2020
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Google supports COVID-19 AI and data analytics projects
by Mollie Javerbaum & Meghan Houghton
Google Keyword Blog | 10-Sep-2020
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Using symptoms search trends to inform COVID-19 research
by Evgeniy Gabrilovich
Google Keyword Blog | 2-Sep-2020
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An update on Exposure Notifications
by Dave Burke
Google Keyword Blog | 31-Jul-2020
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Learn more about anxiety with a self-assessment on Search
by Daniel Gillison, Jr
Google Keyword Blog | 28-May-2020
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Exposure Notification API launches to support public health agencies
by Apple & Google
Google Keyword Blog | 20-May-2020
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Dr. Karen DeSalvo on ‘putting information first’ during COVID-19
by Megan Washam
Google Keyword Blog | 13-May-2020
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Resources for mental health support during COVID-19
by David Feinberg
Google Keyword Blog | 8-May-2020
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Apple and Google partner on COVID-19 contact tracing technology
by Apple & Google
Google Keyword Blog | 10-Apr-2020
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Connecting people to virtual care options
by Julie Black
Google Keyword Blog | 10-Apr-2020
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Support for public health workers fighting COVID-19
by Karen DeSalvo
Google Keyword Blog | 6-Apr-2020
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Helping public health officials combat COVID-19
by Jen Fitzpatrick & Karen DeSalvo
Google Keyword Blog | 3-Apr-2020
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Connecting people with COVID-19 information and resources
by Emily Moxley
Google Keyword Blog | 21-Mar-2020
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COVID-19: How we’re continuing to help
by Sundar Pichai
Google Keyword Blog | 15-Mar-2020
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Coronavirus: How we’re helping
by Sundar Pichai
Google Keyword Blog | 6-Mar-2020
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A new Google Search tool to support women with postpartum depression
by David Feinberg
LinkedIn Blog | 8-Dec-2020
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Advancing health research with Google Health Studies
by Jon Morgan & Paul Eastham
Google Keyword Blog | 9-Dec-2020
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Prepare for medical visits with help from Google and AHRQ
by Dave Greenwood
Google Keyword Blog | 2-Dec-2020
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Lessons Learned from Developing ML for Healthcare
by Yun Liu & Po-Hsuan Cameron Chen
Google AI Blog | 10-Dec-2019
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Tools to help healthcare providers deliver better care
by David Feinberg
Google Keyword Blog | 20-Nov-2019
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Breast cancer and tech...a reason for optimism
by Ruth Porat
Google Keyword Blog | 21-Oct-2019
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DeepMind’s health team joins Google Health
by Dominic King
Google Keyword Blog | 18-Sep-2019
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Meet David Feinberg, head of Google Health
by Google
Google Keyword Blog | 17-Jun-2019
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AI for Social Good in Asia Pacific
by Kent Walter
Google Keyword Blog | 13-Dec-2018
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Gain a deeper understanding of Posttraumatic Stress Disorder on Google
by Paula Schnurr & Teri Brister
Google Keyword Blog | 5-Dec-2017
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Learning more about clinical depression with the PHQ-9 questionnaire
by Mary Giliberti
Google Keyword Blog | 23-Aug-2017
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Partnering on machine learning in healthcare
by Katherine Chou
Google AI Blog | 17-May-2017
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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
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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
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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).
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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).
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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).
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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).
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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).
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How to develop machine learning models for healthcare.
Chen, C. P.-H., Liu, Y., & Peng, L.
Nat. Mater. 18, 410–414 (2019). [readcube]
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Machine Learning in Medicine
Rajkomar, A., Dean, J., & Kohane I.
N. Engl. J. Med. 380:1347-1358 (2019).
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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]
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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).
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When does size matter? -- Promises, pitfalls, and appropriate interpretations of ‘big’ data
Rough K, Thompson J.
Ophthalmology. 125(8):1136-1138 (2018).
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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
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Generating Diverse Synthetic Medical Image Data for Training Machine Learning Models
by Timo Kohlberger & Yuan Liu
Google AI Blog | 19-Feb-2020
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Using Deep Learning to Inform Differential Diagnoses of Skin Diseases
by Yuan Liu & Peggy Bui
Google AI Blog | 12-Sep-2019
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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
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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).
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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]
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DermGAN: Synthetic Generation of Clinical Skin Images with Pathology
Ghorbani, A., Natarajan, V., Coz, D. & Liu, Y.
arXiv [cs.CV] (2019).
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Measuring clinician-machine agreement in differential diagnoses for dermatology
Eng, C., Liu, Y. & Bhatnagar, R.
Br. J. Dermatol. (2019).
Endoscopy
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Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screenings
by Daniel Freedman & Ehud Rivlin
Google AI Blog | 28-Aug-2020
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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
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Healthcare AI systems that put people at the center
by Emma Beede
Google Keyword Blog | 25-Apr-2020
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New milestones in helping prevent eye disease with Verily
by Kasumi Widner & Sunny Virmani
Google Keyword Blog | 25-Feb-2019
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Launching a powerful new screening tool for diabetic eye disease in India
Verily Blog | 25-Feb-2019
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Improving the Effectiveness of Diabetic Retinopathy Models
by Rory Sayres & Jonathan Krause
Google AI Blog | 13-Dec-2018
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A major milestone for the treatment of eye disease
by Mustafa Suleyman
DeepMind Blog | 13-Aug-2018
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Detecting diabetic eye disease with machine learning
by Lily Peng
Google Keyword Blog | 29-Nov-2016
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Deep learning for Detection of Diabetic Eye Disease
by Lily Peng & Varun Gulshan
Google AI Blog | 29-Nov-2016
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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)
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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).
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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).
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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).
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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).
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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).
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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., Raman, R., Peng, L. & Webster, D. R.
JAMA Ophthalmol. (2019).
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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., 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).
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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).
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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]
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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).
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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).
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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).
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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
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Improving the Accuracy of Genomic Analysis with DeepVariant 1.0
by Andrew Carroll & Pi-Chuan Chang
Google AI Blog | 18-Sep-2020
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DeepVariant Accuracy Improvements for Genetic Datatypes
by Pi-Chuan Chang & Lizzie Dorfman
Google AI Blog | 19-Apr-2018
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DeepVariant: Highly Accurate Genomes With Deep Neural Networks
by Mark DePristo & Ryan Poplin
Google AI Blog | 4-Dec-2017
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An AI Resident at work: Suhani Vora and her work on genomics
by Phing Lee
Google Keyword Blog | 17-Nov-2017
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GenomeWarp: an alignment-based variant coordinate transformation
McLean, C. Y., Hwang, Y., Poplin, R. & DePristo, M. A.
Bioinformatics 35, 4389–4391 (2019).
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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]
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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]
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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).
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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
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Joint Speech Recognition and Speaker Diarization via Sequence Transduction
by Laurent El Shafey and Izhak Shafran
16-Aug-2019
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How AI can improve products for people with impaired speech
by Julie Cattiau
Google Keyword Blog | 7-May-2019
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Understanding Medical Conversations
by Katherine Chou and Chung-Cheng Chiu
Google AI Blog | 21-Nov-2017
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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).
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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
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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).
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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
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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).
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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
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A Step Towards Protecting Patients from Medication Errors
by Kathryn Rough & Alvin Rajkomar
Google AI Blog | 2-Apr-2020
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Expanding the Application of Deep Learning to Electronic Health Records
by Alvin Rajkomar & Eyal Oren
Google AI Blog | 22-Jan-2019
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Scaling Streams with Google
by Demis Hassabis & Mustafa Suleyman & Dominic King
DeepMind Blog | 13-Nov-2018
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Deep Learning for Electronic Health Records
by Alvin Rajkomar & Eyal Oren
Google AI Blog | 8-May-2018
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Making Healthcare Data Work Better with Machine Learning
by Patrik Sundberg & Eyal Oren
Google AI Blog | 2-Mar-2018
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Learning to Select Best Forecast Tasks for Clinical Outcome Prediction
Xue Y, Du N, Mottram A, Seneviratne A, Dai AM.
NeurIPS (2020).
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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).
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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).
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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).
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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).
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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).
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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]
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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
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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).
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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).
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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).
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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
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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
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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
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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
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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
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How AI could predict sight-threatening eye conditions
by Terry Spitz & Jim Winkens
Google Keyword Blog | 18-May-2020
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Using AI to predict retinal disease progression
by Jason Yim, Reena Chopra, Jeffrey De Fauw & Joseph Ledsam
DeepMind Blog | 18-May-2020
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Detecting hidden signs of anemia from the eye
by Akinori Mitani
Google Keyword Blog | 28-Jan-2020
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Assessing Cardiovascular Risk Factors with Computer Vision
by Lily Peng
Google AI Blog | 2-Feb-2018
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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., 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
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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).
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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
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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
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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]
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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., 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).
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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.
Nat Biomed Eng (2019). [readcube]
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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
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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).
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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
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Using AI to identify the aggressiveness of prostate cancer
by Kunal Nagpal & Craig Mermel
Google Keyword Blog | 23-Jul-2020
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Generating Diverse Synthetic Medical Image Data for Training Machine Learning Models
by Timo Kohlberger & Yuan Liu
Google AI Blog | 19-Feb-2020
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Building SMILY, a Human-Centric, Similar-Image Search Tool for Pathology
by Narayan Hedge & Carrie Cai
Google AI Blog | 19-July-2019
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Improved Grading of Prostate Cancer Using Deep Learning
by Martin Stumpe & Craig Mermel
Google AI Blog | 16-Nov-2018
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Applying Deep Learning to Metastatic Breast Cancer Detection
by Martin Stumpe & Craig Mermel
Google AI Blog | 12-Oct-2018
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An Augmented Reality Microscope for Cancer Detection
by Martin Stumpe & Craig Mermel
Google AI Blog | 16-Apr-2018
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Assisting Pathologists in Detecting Cancer with Deep Learning
by Martin Stumpe & Lily Peng
Google AI Blog | 3-Mar-2017
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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).
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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).
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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).
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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).
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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]
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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).
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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).
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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).
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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).
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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
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New Insights into Human Mobility with Privacy Preserving Aggregation
by Adam Sadilek & Xerxes Dotiwalla
Google AI Blog | 12-Nov-2019
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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).
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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
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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
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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
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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
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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).
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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).
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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
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Exploring AI for radiotherapy planning with Mayo Clinic
by Cian Hughes
Google Keyword Blog | 29-Oct-2020
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Using AI to improve breast cancer screening
by Shravya Shetty & Daniel Tse
Google Keyword Blog | 1-Jan-2020
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Developing Deep Learning Models for Chest X-rays with Adjudicated Image Labels
by Dave Steiner & Shravya Shetty
Google AI Blog | 3-Dec-2019
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A promising step forward for predicting lung cancer
by Shravya Shetty
Google Keyword Blog | 20-May-2019
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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]
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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., Ding, A., Corrado, G. S., Tse, D. & Shetty, S.
Radiology 191293 (2019).
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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]
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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