Os links fornecidos são apenas para fins de referência e podem estar disponíveis exclusivamente em inglês.
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Posts de blog [mais nos blogs The Keyword e Google AI]
3 ways AI is scaling helpful technologies worldwideby Jeff Dean
Google Keyword Blog | 2-Nov-2022
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Posts de blog
Democratizing access to healthby Karen DeSalvo
Google Keyword Blog | 27-Oct-2022
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Posts de blog
How AI can help in the fight against breast cancerby Nicole Linton
Google Keyword Blog | 21-Oct-2022
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Posts de blog
Google Assistant offers information and hope for Breast Cancer Awareness Monthby Riva Sciuto
Google Keyword Blog | 19-Oct-2022
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Posts de blog
Our work toward health equityby Ivor Horn
Google Keyword Blog | 12-Sep-2022
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Posts de blog
Dr. Von Nguyen’s temperature check on public healthby Lauren Winer
Google Keyword Blog | 25-Aug-2022
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Posts de blog
Suicide prevention resources on Google Searchby Anne Merritt
Google Keyword Blog | 20-Jul-2022
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Posts de blog
Mental health resources you can count onby Megan Jones Bell
Google Keyword Blog | 17-May-2022
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Posts de blog
Raising awareness of the dangers of fentanylby Megan Jones Bell & Garth Graham
Google Keyword Blog | 10-May-2022
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Posts de blog
The Check Up: our latest health AI developmentsby Greg Corrado
Google AI Blog | 24-Mar-2022
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Posts de blog
The Check Up: helping people live healthier livesby Karen DeSalvo
Google Keyword Blog | 24-Mar-2022
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Posts de blog
Our FHIR SDK for Android Developersby Katherine Chou & Sudhi Herle
Android Developers Blog | 24-Mar-2022
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Posts de blog
Extending Care Studio with a new healthcare partnershipby Paul Muret
Google Keyword Blog | 15-Mar-2022
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Posts de blog
Take a look at Conditions, our new feature in Care Studioby Paul Muret
Google Keyword Blog | 8-Mar-2022
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Posts de blog
Google Research: Themes from 2021 and Beyondby Jeff Dean
Google Research Blog | 11-Jan-2022
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Posts de blog
Working with the WHO to power digital health appsby Fred Hersch & Jing Tang
Google Keyword Blog | 8-Dec-2021
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Posts de blog
Making healthcare options more accessible on Searchby Hema Budaraju
Google Keyword Blog | 2-Dec-2021
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Posts de blog
HLTH: Building on our commitments in healthby Karen DeSalvo
Google Keyword Blog | 17-Oct-2021
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Posts de blog
When it comes to mental health, what are we searching for?by Alicia Cormie
Google Keyword Blog | 6-May-2021
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Posts de blog
Dr. Ivor Horn talks about technology and health equityby Alicia Cormie
Google Keyword Blog | 16-Apr-2021
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Posts de blog
Our Care Studio pilot is expanding to more cliniciansby Paul Muret
Google Keyword Blog | 23- Feb-2021
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Posts de blog
Google Research: Looking Back at 2020, and Forward to 2021by Jeff Dean
Google Research Blog | 12-Jan-2021
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Posts de blog
A new Google Search tool to support women with postpartum depressionby David Feinberg
LinkedIn Blog | 8-Dec-2020
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Posts de blog
Prepare for medical visits with help from Google and AHRQby Dave Greenwood
Google Keyword Blog | 2-Dec-2020
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Posts de blog
A Collaborative Approach to Shaping Successful UX Critique Practicesby Anna Lurchenko
Google Design Blog | 29-Jul-2020
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Posts de blog
Learn more about anxiety with a self-assessment on Searchby Daniel Gillison, Jr
Google Keyword Blog | 28-May-2020
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Posts de blog
Google Research: Looking Back at 2019, and Forward to 2020 and Beyondby Jeff Dean
Google Research Blog | 9-Jan-2020
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Posts de blog
Lessons Learned from Developing ML for Healthcareby Yun Liu & Po-Hsuan Cameron Chen
Google AI Blog | 10-Dec-2019
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Posts de blog
Tools to help healthcare providers deliver better careby David Feinberg
Google Keyword Blog | 20-Nov-2019
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Posts de blog
Breast cancer and tech...a reason for optimismby Ruth Porat
Google Keyword Blog | 21-Oct-2019
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Posts de blog
DeepMind’s health team joins Google Healthby Dominic King
Google Keyword Blog | 18-Sep-2019
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Posts de blog
Looking Back at Google’s Research Efforts in 2018by Jeff Dean
Google Research Blog | 15-Jan-2019
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Posts de blog
Meet David Feinberg, head of Google Healthby Google
Google Keyword Blog | 17-Jun-2019
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Posts de blog
AI for Social Good in Asia Pacificby Kent Walter
Blog The Keyword do Google | 13-dez-2018
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Posts de blog
The Google Brain Team — Looking Back on 2017 (Part 2 of 2)by Jeff Dean
Google Research Blog | 12-Jan-2018
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Posts de blog
Gain a deeper understanding of Posttraumatic Stress Disorder on Googleby Paula Schnurr & Teri Brister
Google Keyword Blog | 5-Dec-2017
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Posts de blog
Learning more about clinical depression with the PHQ-9 questionnaireby Mary Giliberti
Google Keyword Blog | 23-Aug-2017
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Posts de blog
Partnering on machine learning in healthcareby Katherine Chou
Google AI Blog | 17-May-2017
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Posts de blog
The Google Brain Team — Looking Back on 2016by Jeff Dean
Google Research Blog | 12-Jan-2017
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Posts de blog sobre Covid-19
Supporting evolving COVID information needsby Hema Budaraju
Google Keyword Blog | 16-Jun-2022
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Posts de blog sobre Covid-19 [more at Google Keyword Blog]
Group effort: How we helped launch an NYC vaccine siteby Lauren Gallagher
Google Keyword Blog | 11-Feb-2022
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Posts de blog sobre Covid-19 [more at Google Keyword Blog]
This year, we searched for ways to stay healthyby Hema Budaraju
Google Keyword Blog | 8-Dec-2021
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Posts de blog sobre Covid-19
New tools to support vaccine access and distributionby Tomer Shekel
Google Keyword Blog | 9-Jun-2021
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Posts de blog sobre Covid-19
An update on our COVID response prioritiesby the COVID Response team, Google India
Google India Blog | 10-May-2021
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Posts de blog sobre Covid-19
Our commitment to COVID-19 vaccine equityby Karen DeSalvo
Google Keyword Blog | 15-Apr-2021[Spanish version]
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Posts de blog sobre Covid-19
How anonymized data helps fight against diseaseby Stephen Ratcliffe
Google Keyword Blog | 24-Feb-2021
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Posts de blog sobre Covid-19
How we’re helping get vaccines to more peopleby Sundar Pichai
Google Keyword Blog | 25-Jan-2021
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Posts de blog sobre Covid-19
Exposure Notifications: end of year updateby Steph Hannon
Google Keyword Blog | 11-Dec-2020
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Posts de blog sobre Covid-19
How you'll find accurate and timely information on COVID-19 vaccinesby Karen DeSalvo & Kristie Canegallo
Google Keyword Blog | 10-Dec-2020
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Posts de blog sobre Covid-19
How I’m giving thanks (and staying safe) this Thanksgivingby Karen DeSalvo
Google Keyword Blog | 24-Nov-2020 [Spanish version]
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Posts de blog sobre Covid-19
A Q&A on coronavirus vaccinesGoogle Keyword Blog
10-Nov-2020
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Posts de blog sobre Covid-19
An update on our efforts to help Americans navigate COVID-19by Ruth Porat
Google Keyword Blog | 27-Oct-2020
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Posts de blog sobre Covid-19
Making data useful for public healthby Katherine Chou
Google Keyword Blog | 17-Sept-2020
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Posts de blog sobre Covid-19
Google supports COVID-19 AI and data analytics projectsby Mollie Javerbaum & Meghan Houghton
Google Keyword Blog | 10-Sep-2020
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Posts de blog sobre Covid-19
Using symptoms search trends to inform COVID-19 researchby Evgeniy Gabrilovich
Google Keyword Blog | 2-Sep-2020
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Posts de blog sobre Covid-19
An update on Exposure Notificationsby Dave Burke
Google Keyword Blog | 31-Jul-2020
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Posts de blog sobre Covid-19
Exposure Notification API launches to support public health agenciesby Apple & Google
Google Keyword Blog | 20-May-2020
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Posts de blog sobre Covid-19
Dr. Karen DeSalvo on ‘putting information first’ during COVID-19by Megan Washam
Google Keyword Blog | 13-May-2020
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Posts de blog sobre Covid-19
Resources for mental health support during COVID-19by David Feinberg
Google Keyword Blog | 8-May-2020
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Posts de blog sobre Covid-19
Apple and Google partner on COVID-19 contact tracing technologyby Apple & Google
Google Keyword Blog | 10-Apr-2020
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Posts de blog sobre Covid-19
Connecting people to virtual care optionsby Julie Black
Google Keyword Blog | 10-Apr-2020
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Posts de blog sobre Covid-19
Support for public health workers fighting COVID-19by Karen DeSalvo
Google Keyword Blog | 6-Apr-2020
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Posts de blog sobre Covid-19
Helping public health officials combat COVID-19by Jen Fitzpatrick & Karen DeSalvo
Google Keyword Blog | 3-Apr-2020
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Posts de blog sobre Covid-19
Connecting people with COVID-19 information and resourcesby Emily Moxley
Google Keyword Blog | 21-Mar-2020
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Posts de blog sobre Covid-19
COVID-19: How we’re continuing to helpby Sundar Pichai
Google Keyword Blog | 15-Mar-2020
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Posts de blog sobre Covid-19
Coronavirus: How we’re helpingby Sundar Pichai
Google Keyword Blog | 6-Mar-2020
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Avaliações
Medicine’s Role in Reimagining Public Health: Reuniting Panacea and HygeiaDeSalvo, K. B., Kadakia, K. T. & Chokshi, D. A.
JAMA Health Forum 2, e214051–e214051 (2021).
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Avaliações
Modernizing Public Health Data Systems: Lessons From the Health Information Technology for Economic and Clinical Health (HITECH) ActKadakia, K. T., Howell, M. D. & DeSalvo, K. B.
JAMA 326, 385–386 (2021).
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Avaliações
Public Health 3.0 After COVID-19-Reboot or Upgrade?DeSalvo, K. B. & Kadakia, K. T.
Am. J. Public Health 111, S179–S181 (2021).
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Avaliações
A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AISounderajah, V., Ashrafian, H., Rose, S., Shah, N. H., Ghassemi, M., Golub, R., Kahn, C. E., Jr, Esteva, A., Karthikesalingam, A., Mateen, B., Webster, D., Milea, D., Ting, D., Treanor, D., Cushnan, D., King, D., McPherson, D., Glocker, B., Greaves, F., Harling, L., Ordish, J., Cohen, J. F., Deeks, J., Leeflang, M., Diamond, M., McInnes, M. D. F., McCradden, M., Abràmoff, M. D., Normahani, P., Markar, S. R., Chang, S., Liu, X., Mallett, S., Shetty, S., Denniston, A., Collins, G. S., Moher, D., Whiting, P., Bossuyt, P. M. & Darzi, A.
Nat. Med. (2021).
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Avaliações
Evaluation of artificial intelligence on a reference standard based on subjective interpretationChen, P.-H. C., Mermel, C. H. & Liu, Y.
The Lancet Digital Health (2021). doi:10.1016/S2589-7500(21)00216-8
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Avaliações
Artificial Intelligence in MedicineKelly, C. J., Brown, A. P. Y. & Taylor, J. A.
(eds. Lidströmer, N. & Ashrafian, H.) 1–18 (Springer International Publishing, 2021).
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Avaliações
Challenges of Accuracy in Germline Clinical Sequencing DataPoplin, R., Zook, J. M. & DePristo, M.
JAMA 326, 268–269 (2021).
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Avaliações
Retinal detection of kidney disease and diabetesMitani, A., Hammel, N. & Liu, Y.
Nature Biomedical Engineering 1–3 (2021). [readcube]
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Avaliações
Deep learning-enabled medical computer visionEsteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J. & Socher, R.
npj Digital Medicine 4, 5 (2021).
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Avaliações
Closing the translation gap: AI applications in digital pathologySteiner, D. F., Chen, P.-H. C. & Mermel, C. H.
Steiner, D. F., Chen, P.-H. C. & Mermel, C. H.
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Avaliações
Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic retinopathy from retinal fundus photographsLiu, Y., Yang, L., Phene, S. & Peng, L.
Artificial Intelligence in Medicine 247–264 (2021).
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Avaliações
Resonate: Reaching Excellence Through Equity, Diversity, and Inclusion in ISMRMWarnert, E. A. H., Kasper, L., Meltzer, C. C., Lightfoote, J. B., Bucknor, M. D., Haroon, H., Duggan, G., Gowland, P., Wald, L., Miller, K. L., Morris, E. A. & Anazodo, U. C.
J. Magn. Reson. Imaging (2020). doi:10.1002/jmri.27476 [readcube]
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Avaliações
Current and future applications of artificial intelligence in pathology: a clinical perspectiveRakha, 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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Avaliações
Artificial intelligence, machine learning and deep learning for eye care specialistsSayres, R., Hammel, N. & Liu, Y.
Annals of Eye Science 5, 18–18 (2020).
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Avaliações
Artificial intelligence in digital breast pathology: Techniques and applicationsIbrahim, 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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Avaliações
How to Read Articles That Use Machine Learning: Users’ Guides to the Medical LiteratureLiu, Y., Chen, P.-H. C., Krause, J. & Peng, L.
JAMA 322, 1806–1816 (2019). [readcube]
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Avaliações
Key challenges for delivering clinical impact with artificial intelligenceKelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D.
BMC Med. 17, 195 (2019).
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Avaliações
Ensuring Fairness in Machine Learning to Advance Health EquityRajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H.
Ann. Intern. Med. 169(12):866-872 (2018).
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Avaliações
Artificial Intelligence Approach in MelanomaCuriel-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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Avaliações
How to develop machine learning models for healthcareChen, C. P.-H., Liu, Y., & Peng, L.
Nat. Mater. 18, 410–414 (2019). [readcube]
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Avaliações
Machine Learning in MedicineRajkomar, A., Dean, J., & Kohane I.
N. Engl. J. Med. 380:1347-1358 (2019).
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Avaliações
A guide to deep learning in healthcareEsteva, 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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Avaliações
When does size matter? -- Promises, pitfalls, and appropriate interpretations of ‘big’ dataRough K, Thompson J.
Ophthalmology. 125(8):1136-1138 (2018).
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Avaliações
Resolving the Productivity Paradox of Health Information Technology: A Time for OptimismWachter, R. M., Howell, M. D.
JAMA 320(1):25-26 (2018).
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Posts de blog
How Underspecification Presents Challenges for Machine Learningby Alex D’Amour and Katherine Heller
Google AI Blog | 18-Oct-2021
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Posts de blog
Self-Supervised Learning Advances Medical Image Classificationby Shekoofeh Azizi
Google AI Blog | 13-Oct-2021
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Publicações
Iterative Quality Control Strategies for Expert Medical Image LabelingFreeman, B., Hammel, N., Phene, S., Huang, A., Ackermann, R., Kanzheleva, O., Hutson, M., Taggart, C., Duong, Q. & Sayres, R.
HCOMP 9, 60–71 (2021).
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Publicações
Big Self-Supervised Models Advance Medical Image ClassificationAzizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., Natarajan, V. & Norouzi, M.
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 3478–3488 (2021).
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Publicações
Privacy-first health research with federated learningSadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., MacFarlane, J., Vullikanti, A., Marathe, M., Eastham, P., Brownstein, J. S., Arcas, B. A. Y., Howell, M. D. & Hernandez, J.
NPJ Digit Med 4, 132 (2021).
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Publicações
Supervised Transfer Learning at Scale for Medical ImagingMustafa, B., Loh, A., Freyberg, J., MacWilliams, P., Karthikesalingam, A., Houlsby, N. & Natarajan, V.
arXiv [cs.CV] (2021).
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Publicações
Big Self-Supervised Models Advance Medical Image ClassificationAzizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., Natarajan, V. & Norouzi, M.
arXiv [eess.IV] (2021).
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Publicações
Underspecification Presents Challenges for Credibility in Modern Machine LearningD’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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Publicações
Contrastive Training for Improved Out-of-Distribution DetectionWinkens, J., Bunel, R., Roy, A. G., Stanforth, R., Natarajan, V., Ledsam, J. R., MacWilliams, P., Kohli, P., Karthikesalingam, A., Kohl, S., Cemgil, T., Ali Eslami, S. M. & Ronneberger, O.
arXiv [cs.LG] (2020).
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Publicações
Customization scenarios for de-identification of clinical notesHartman, T., Howell, M., Dean, J., Hoory, S., Slyper, R., Laish, I., Gilon, O, Vainstein, D., Corrado, G., Chou, K., Po, M., Williams, J., Ellis, S., Bee, G., Hassidim, A., Amira, R., Beryozkin, G., Szpektor, I., & Matias, Y.
BMC (2020).
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Posts de blog
How DermAssist uses TensorFlow.js for on-device image quality checksby Miles Hutson & Aaron Loh
TensorFlow Blog | 11-Oct-2021
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Posts de blog
Using AI to help find answers to common skin conditionsby Peggy Bui & Yuan Liu
Google Keyword Blog | 18-May-2021
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Posts de blog
AI assists doctors in interpreting skin conditionsby Ayush Jain & Peggy Bui
Google Keyword Blog | 28-Apr-2021
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Posts de blog
Generating Diverse Synthetic Medical Image Data for Training Machine Learning Modelsby Timo Kohlberger & Yuan Liu
Google AI Blog | 19-Feb-2020
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Posts de blog
Using Deep Learning to Inform Differential Diagnoses of Skin Diseasesby Yuan Liu & Peggy Bui
Google AI Blog | 12-Sep-2019
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Publicações
Machine learning for clinical operations improvement via case triagingHuang, S. J., Liu, Y., Kanada, K., Corrado, G. S., Webster, D. R., Peng, L., Bui, P. & Liu, Y
Skin Health and Disease (2021)
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Publicações
Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditionsGuha Roy, A., Ren, J., Azizi, S., Loh, A., Natarajan, V., Mustafa, B., Pawlowski, N., Freyberg, J., Liu, Y., Beaver, Z., Vo, N., Bui, P., Winter, S., MacWilliams, P., Corrado, G. S., Telang, U., Liu, Y., Cemgil, T., Karthikesalingam, A., Lakshminarayanan, B. & Winkens, J.
Med. Image Analysis. 75, 102274 (2021). [reading link]
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Publicações
Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology PracticesWeng, W.-H., Deaton, J., Natarajan, V., Elsayed, G. F. & Liu, Y
JAMA Netw Open 4, e217249–e217249 (2021).
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Publicações
Addressing the Real-world Class Imbalance Problem in DermatologyWeng, W.-H., Deaton, J., Natarajan, V., Elsayed, G. F. & Liu, Y.
Machine Learning for Health NeurIPS Workshop (ML4H), PMLR 136:415-429 (2020).
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Publicações
Agreement Between Saliency Maps and Human-Labeled Regions of Interest: Applications to Skin Disease ClassificationSingh, 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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Publicações
A deep learning system for differential diagnosis of skin diseasesLiu, 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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Publicações
DermGAN: Synthetic Generation of Clinical Skin Images with PathologyGhorbani, A., Natarajan, V., Coz, D. & Liu, Y.
Machine Learning for Health NeurIPS Workshop (ML4H), PMLR 116:155-170 (2020).
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Publicações
Measuring clinician-machine agreement in differential diagnoses for dermatologyEng, C., Liu, Y. & Bhatnagar, R.
Br. J. Dermatol. (2019). readcube
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Posts de blog
Improved Detection of Elusive Polyps via Machine Learningby Yossi Matias & Ehud Rivlin
Google AI Blog | 5-Aug-2021
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Posts de blog
Verily Opens New R&D Center in Israel Focused on the Application of AI in Healthcareby Robin Suchan
Verily Press | 5-Aug-2021
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Posts de blog
Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screeningsby Daniel Freedman & Ehud Rivlin
Google AI Blog | 28-Aug-2020
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Publicações
Artificial intelligence for phase recognition in complex laparoscopic cholecystectomyGolany, T., Aides, A., Freedman, D., Rabani, N., Liu, Y., Rivlin, E., Corrado, G. S., Matias, Y., Khoury, W., Kashtan, H. & Reissman, P.
Surg. Endosc. (2022).
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Publicações
Detection of elusive polyps via a large-scale artificial intelligence system (with videos)Livovsky, D. M., Veikherman, D., Golany, T., Aides, A., Dashinsky, V., Rabani, N., Ben Shimol, D., Blau, Y., Katzir, L., Shimshoni, I., Liu, Y., Segol, O., Goldin, E., Corrado, G., Lachter, J., Matias, Y., Rivlin, E. & Freedman, D.
Gastrointest. Endosc. (2021).
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Publicações
Detecting Deficient Coverage in ColonoscopiesFreedman, 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).
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Posts de blog
Healthcare AI systems that put people at the centerby Emma Beede
Google Keyword Blog | 25-Apr-2020
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Posts de blog
New milestones in helping prevent eye disease with Verilyby Kasumi Widner & Sunny Virmani
Google Keyword Blog | 25-Feb-2019
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Posts de blog
Launching a powerful new screening tool for diabetic eye disease in IndiaVerily Blog | 25-Feb-2019
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Posts de blog
Improving the Effectiveness of Diabetic Retinopathy Modelsby Rory Sayres & Jonathan Krause
Google AI Blog | 13-Dec-2018
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Posts de blog
A major milestone for the treatment of eye diseaseby Mustafa Suleyman
DeepMind Blog | 13-Aug-2018
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Posts de blog
Detecting diabetic eye disease with machine learningby Lily Peng
Google Keyword Blog | 29-Nov-2016
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Posts de blog
Deep learning for Detection of Diabetic Eye Diseaseby Lily Peng & Varun Gulshan
Google AI Blog | 29-Nov-2016
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Publicações
Redesigning Clinical Pathways for Immediate Diabetic Retinopathy Screening ResultsPedersen Elin Rønby, Cuadros Jorge, Khan Mahbuba, Fleischmann Sybille, Wolff Gregory, Hammel Naama, Liu Yun & Leung Geoffrey.
NEJM Catalyst (2021).
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Publicações
Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep LearningWilson, M., Chopra, R., Wilson, M. Z., Cooper, C., MacWilliams, P., Liu, Y., Wulczyn, E., Florea, D., Hughes, C. O., Karthikesalingam, A., Khalid, H., Vermeirsch, S., Nicholson, L., Keane, P. A., Balaskas, K. & Kelly, C. J.
JAMA Ophthalmol. (2021).
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Publicações
Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human GradersLimwattanayingyong, J., Nganthavee, V., Seresirikachorn, K., Singalavanija, T., Soonthornworasiri, N., Ruamviboonsuk, V., Rao, C., Raman, R., Grzybowski, A., Schaekermann, M., Peng, L. H., Webster, D. R., Semturs, C., Krause, J., Sayres, R., Hersch, F., Tiwari, R., Liu, Y. & Ruamviboonsuk, P.
Journal of Diabetes Research, 1–8 (2020).
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Publicações
Improving medical annotation quality to decrease labeling burden using stratified noisy cross-validationHsu J, Phene S, Mitani A, Luo J, Hammel N, Krause J, Sayres R.
in ACM-CHIL [arXiv](2020)
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Publicações
Adherence to ophthalmology referral, treatment and follow-up after diabetic retinopathy screening in the primary care settingBresnick, 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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Publicações
A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic RetinopathyBeede, 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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Publicações
Expert Discussions Improve Comprehension of Difficult Cases in Medical Image AssessmentSchaekermann, 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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Publicações
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus PhotographsPhene, S., Dunn, R. C., Hammel, N., Liu, Y., Krause, J., Kitade, N., Schaekermann, M., Sayres, R., Wu, D. J., Bora, A., Semturs, C., Misra, A., Huang, A. E., Spitze, A., Medeiros, F. A., Maa, A. Y., Gandhi, M., Corrado, G. S., Peng, L. & Webster, D. R.
Ophthalmology 126, 1627–1639 (2019).
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Publicações
Remote Tool-Based Adjudication for Grading Diabetic RetinopathySchaekermann, 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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Publicações
Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in IndiaGulshan, 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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Publicações
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening programRuamviboonsuk, 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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Publicações
Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic RetinopathySayres, 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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Publicações
Clinically applicable deep learning for diagnosis and referral in retinal diseaseFauw, 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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Publicações
Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic RetinopathyKrause, 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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Publicações
Blind spots in telemedicine: a qualitative study of staff workarounds to resolve gaps in diabetes managementBouskill, K., Smith-Morris, C., Bresnick, G., Cuadros, J. & Pedersen, E. R.
BMC Health Services Research 18, (2018).
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Publicações
Diabetic Retinopathy and the Cascade into Vision LossSmith-Morris, C., Bresnick, G. H., Cuadros, J., Bouskill, K. E. & Pedersen, E. R.
Med. Anthropol. 39, 109–122 (2018).
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Publicações
Who Said What: Modeling Individual Labelers Improves ClassificationGuan, M., Gulshan, V., Dai, A, Hinton, G.
AAAI Conference on Artificial Intelligence (2018).
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Publicações
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographsGulshan, 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).
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Posts de blog
An ML-Based Framework for COVID-19 Epidemiologyby Joel Shor & Sercan Arik
Google AI Blog | 13-Oct-2021
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Posts de blog
Google Cloud, Harvard Global Health Institute release improved COVID-19 Public Forecasts, share lessons learnedby Tomas Pfister
Google Cloud Blog | 15-Nov-2020
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Posts de blog
Google Cloud AI and Harvard Global Health Institute Collaborate on new COVID-19 forecasting modelby Dario Sava
Google Cloud Blog | 3-Aug-2020
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Publicações
Algorithmic fairness in pandemic forecasting: lessons from COVID-19Tsai, T. C., Arik, S., Jacobson, B. H., Yoon, J., Yoder, N., Sava, D., Mitchell, M., Graham, G. & Pfister, T.
NPJ Digit Med 5, 59 (2022).
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Publicações
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United StatesCramer, E. Y., Ray, E. L., Lopez, V. K., Bracher, J., Brennen, A., Castro Rivadeneira, A. J., Gerding, A., Gneiting, T., House, K. H., Huang, Y., Jayawardena, D., Kanji, A. H., Khandelwal, A., Le, K., Mühlemann, A., Niemi, J., Shah, A., Stark, A., Wang, Y., Wattanachit, N., Zorn, M. W., Gu, Y., Jain, S., Bannur, N., Deva, A., Kulkarni, M., Merugu, S., Raval, A., Shingi, S., Tiwari, A., White, J., Abernethy, N. F., Woody, S., Dahan, M., Fox, S., Gaither, K., Lachmann, M., Meyers, L. A., Scott, J. G., Tec, M., Srivastava, A., George, G. E., Cegan, J. C., Dettwiller, I. D., England, W. P., Farthing, M. W., Hunter, R. H., Lafferty, B., Linkov, I., Mayo, M. L., Parno, M. D., Rowland, M. A., Trump, B. D., Zhang-James, Y., Chen, S., Faraone, S. V., Hess, J., Morley, C. P., Salekin, A., Wang, D., Corsetti, S. M., Baer, T. M., Eisenberg, M. C., Falb, K., Huang, Y., Martin, E. T., McCauley, E., Myers, R. L., Schwarz, T., Sheldon, D., Gibson, G. C., Yu, R., Gao, L., Ma, Y., Wu, D., Yan, X., Jin, X., Wang, Y.-X., Chen, Y., Guo, L., Zhao, Y., Gu, Q., Chen, J., Wang, L., Xu, P., Zhang, W., Zou, D., Biegel, H., Lega, J., McConnell, S., Nagraj, V. P., Guertin, S. L., Hulme-Lowe, C., Turner, S. D., Shi, Y., Ban, X., Walraven, R., Hong, Q.-J., Kong, S., van de Walle, A., Turtle, J. A., Ben-Nun, M., Riley, S., Riley, P., Koyluoglu, U., DesRoches, D., Forli, P., Hamory, B., Kyriakides, C., Leis, H., Milliken, J., Moloney, M., Morgan, J., Nirgudkar, N., Ozcan, G., Piwonka, N., Ravi, M., Schrader, C., Shakhnovich, E., Siegel, D., Spatz, R., Stiefeling, C., Wilkinson, B., Wong, A., Cavany, S., España, G., Moore, S., Oidtman, R., Perkins, A., Kraus, D., Kraus, A., Gao, Z., Bian, J., Cao, W., Lavista Ferres, J., Li, C., Liu, T.-Y., Xie, X., Zhang, S., Zheng, S., Vespignani, A., Chinazzi, M., Davis, J. T., Mu, K., Pastore Y Piontti, A., Xiong, X., Zheng, A., Baek, J., Farias, V., Georgescu, A., Levi, R., Sinha, D., Wilde, J., Perakis, G., Bennouna, M. A., Nze-Ndong, D., Singhvi, D., Spantidakis, I., Thayaparan, L., Tsiourvas, A., Sarker, A., Jadbabaie, A., Shah, D., Della Penna, N., Celi, L. A., Sundar, S., Wolfinger, R., Osthus, D., Castro, L., Fairchild, G., Michaud, I., Karlen, D., Kinsey, M., Mullany, L. C., Rainwater-Lovett, K., Shin, L., Tallaksen, K., Wilson, S., Lee, E. C., Dent, J., Grantz, K. H., Hill, A. L., Kaminsky, J., Kaminsky, K., Keegan, L. T., Lauer, S. A., Lemaitre, J. C., Lessler, J., Meredith, H. R., Perez-Saez, J., Shah, S., Smith, C. P., Truelove, S. A., Wills, J., Marshall, M., Gardner, L., Nixon, K., Burant, J. C., Wang, L., Gao, L., Gu, Z., Kim, M., Li, X., Wang, G., Wang, Y., Yu, S., Reiner, R. C., Barber, R., Gakidou, E., Hay, S. I., Lim, S., Murray, C., Pigott, D., Gurung, H. L., Baccam, P., Stage, S. A., Suchoski, B. T., Prakash, B. A., Adhikari, B., Cui, J., Rodríguez, A., Tabassum, A., Xie, J., Keskinocak, P., Asplund, J., Baxter, A., Oruc, B. E., Serban, N., Arik, S. O., Dusenberry, M., Epshteyn, A., Kanal, E., Le, L. T., Li, C.-L., Pfister, T., Sava, D., Sinha, R., Tsai, T., Yoder, N., Yoon, J., Zhang, L., Abbott, S., Bosse, N. I., Funk, S., Hellewell, J., Meakin, S. R., Sherratt, K., Zhou, M., Kalantari, R., Yamana, T. K., Pei, S., Shaman, J., Li, M. L., Bertsimas, D., Skali Lami, O., Soni, S., Tazi Bouardi, H., Ayer, T., Adee, M., Chhatwal, J., Dalgic, O. O., Ladd, M. A., Linas, B. P., Mueller, P., Xiao, J., Wang, Y., Wang, Q., Xie, S., Zeng, D., Green, A., Bien, J., Brooks, L., Hu, A. J., Jahja, M., McDonald, D., Narasimhan, B., Politsch, C., Rajanala, S., Rumack, A., Simon, N., Tibshirani, R. J., Tibshirani, R., Ventura, V., Wasserman, L., O’Dea, E. B., Drake, J. M., Pagano, R., Tran, Q. T., Ho, L. S. T., Huynh, H., Walker, J. W., Slayton, R. B., Johansson, M. A., Biggerstaff, M. & Reich, N. G.
Proc. Natl. Acad. Sci. U. S. A. 119, e2113561119 (2022).
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Publicações
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and JapanArık, S. Ö., Shor, J., Sinha, R., Yoon, J., Ledsam, J. R., Le, L. T., Dusenberry, M. W., Yoder, N. C., Popendorf, K., Epshteyn, A., Euphrosine, J., Kanal, E., Jones, I., Li, C.-L., Luan, B., Mckenna, J., Menon, V., Singh, S., Sun, M., Ravi, A. S., Zhang, L., Sava, D., Cunningham, K., Kayama, H., Tsai, T., Yoneoka, D., Nomura, S., Miyata, H. & Pfister, T.
NPJ Digit Med 4, 146 (2021).
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Publicações
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural NetworksKapoor, A., Ben, X., Liu, L., Perozzi, B., Barnes, M., Blais, M. & O’Banion, S.
arXiv [cs.LG] (2020).
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Publicações
Interpretable Sequence Learning for Covid-19 ForecastingArik, Li, Yoon, Sinha, Epshteyn, Le, Menon, Singh, Zhang, Nikoltchev, Sonthalia, Nakhost, Kanal & Pfister.
Adv. Neural Inf. Process. Syst. 2020.
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Posts de blog
Enhanced Sleep Sensing in Nest Hubby Michael Dixon & Reena Singhal Lee
Google AI Blog | 9-Nov-2021
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Posts de blog
Need a better night’s sleep? Meet the new Nest Hubby Ashton Udall
Google Keyword Blog | 16-Mar-2021
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Posts de blog
Contactless Sleep Sensing in Nest Hubby Michael Dixon & Reena Singhal Lee
Google AI Blog | 16-Mar-2021
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Posts de blog
Take a pulse on health and wellness with your phoneby Shwetak Patel
Google Keyword Blog | 4-Feb-2021
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Publicações
Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithmsBae, S., Borac, S., Emre, Y., Wang, J., Wu, J., Kashyap, M., Kang, S.-H., Chen, L., Moran, M., Cannon, J., Teasley, E. S., Chai, A., Liu, Y., Wadhwa, N., Krainin, M., Rubinstein, M., Maciel, A., McConnell, M. V., Patel, S., Corrado, G. S., Taylor, J. A., Zhan, J. & Po, M. J.
bioRxiv (2021).
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Publicações
Sleep-wake Detection With a Contactless, Bedside Radar Sleep Sensing SystemDixon, M., Schneider, L. D., Yu, J., Hsu, J., Pathak, A., Shin, D., Lee, R. S., Malhotra, M., Mixter, K., McConnell, M. V., Taylor, J. A., Patel, S. N.,
Google Whitepaper (2021).
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Posts de blog [more at Fitbit Blog]
Google Pixel Watch: Help by Google, health by Fitbitby Sandeep Waraich
Google Keyword Blog | 6-Oct-2022
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Posts de blog
8 things to try now on Fitbit Sense 2 and Versa 4by TJ Varghese
Google Keyword Blog | 29-Sep-2022
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Posts de blog
Kick-start your fitness routine with Fitbit Inspire 3by The Fitbit Team
Google Keyword Blog | 24-Aug-2022
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Posts de blog
Fitbit’s fall lineup: helping you live your healthiest lifeby TJ Varghese
Google Keyword Blog | 24-Aug-2022
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Posts de blog
Manage your health and fitness with Fitbit Versa 4 and Sense 2by The Fitbit Team
Google Keyword Blog | 24-Aug-2022
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Posts de blog
Improve your ZZZs with Fitbit Premium Sleep Profileby The Fitbit Team
Blog The Keyword do Google | 22-jun-2022
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Posts de blog
New Fitbit feature makes AFib detection more accessibleby The Fitbit Team
Google Keyword Blog | 11-Apr-2022
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Publicações
Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart StudyLubitz, S. A., Faranesh, A. Z., Selvaggi, C., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., McConnell, M. V., Pantelopoulos, A. & Foulkes, A. S.
Circulation (2022).
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Publicações
Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19Natarajan, A., Su, H.-W. & Heneghan, C.
Front. Physiol. 13, 898251 (2022).
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Publicações
Measurement of respiratory rate using wearable devices and applications to COVID-19 detectionNatarajan, A., Su, H.-W., Heneghan, C., Blunt, L., O’Connor, C. & Niehaus, L.
NPJ Digit Med 4, 136 (2021).
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Posts de blog
A new genome sequencing tool powered with our technologyby Andrew Caroll
Google Keyword Blog | 26-Oct-2022
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Posts de blog [mais no blog do DeepVariant (em inglês)]
Advancing genomics to better understand and treat diseaseby Andrew Carroll & Pi-Chuan Chang
Google Keyword Blog | 13-Jan-2022
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Posts de blog
DeepNull: an open-source method to improve the discovery power of genetic association studiesby Farhad Hormozdiari & Andrew Carroll
Google Open Source Blog | 11-Jan-2022
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Posts de blog
Improving Genomic Discovery with Machine Learningby Andrew Carroll & Cory McLean
Google AI Blog | 23-Jun-2021
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Posts de blog
Improving the Accuracy of Genomic Analysis with DeepVariant 1.0by Andrew Carroll & Pi-Chuan Chang
Google AI Blog | 18-Sep-2020
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Posts de blog
DeepVariant Accuracy Improvements for Genetic Datatypesby Pi-Chuan Chang & Lizzie Dorfman
Google AI Blog | 19-Apr-2018
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Posts de blog
DeepVariant: Highly Accurate Genomes With Deep Neural Networksby Mark DePristo & Ryan Poplin
Google AI Blog | 4-Dec-2017
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Posts de blog
An AI Resident at work: Suhani Vora and her work on genomicsby Phing Lee
Google Keyword Blog | 17-Nov-2017
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Publicações
Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencingGoenka, S. D., Gorzynski, J. E., Shafin, K., Fisk, D. G., Pesout, T., Jensen, T. D., Monlong, J., Chang, P.-C., Baid, G., Bernstein, J. A., Christle, J. W., Dalton, K. P., Garalde, D. R., Grove, M. E., Guillory, J., Kolesnikov, A., Nattestad, M., Ruzhnikov, M. R. Z., Samadi, M., Sethia, A., Spiteri, E., Wright, C. J., Xiong, K., Zhu, T., Jain, M., Sedlazeck, F. J., Carroll, A., Paten, B. & Ashley, E. A.
Nat. Biotechnol. (2022).
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Publicações
Ultrarapid Nanopore Genome Sequencing in a Critical Care SettingGorzynski, J. E., Goenka, S. D., Shafin, K., Jensen, T. D., Fisk, D. G., Grove, M. E., Spiteri, E., Pesout, T., Monlong, J., Baid, G., Bernstein, J. A., Ceresnak, S., Chang, P.-C., Christle, J. W., Chubb, H., Dalton, K. P., Dunn, K., Garalde, D. R., Guillory, J., Knowles, J. W., Kolesnikov, A., Ma, M., Moscarello, T., Nattestad, M., Perez, M., Ruzhnikov, M. R. Z., Samadi, M., Setia, A., Wright, C., Wusthoff, C. J., Xiong, K., Zhu, T., Jain, M., Sedlazeck, F. J., Carroll, A., Paten, B. & Ashley, E. A.
N. Engl. J. Med. (2022).
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Publicações
Pangenomics enables genotyping of known structural variants in 5202 diverse genomesSirén, J., Monlong, J., Chang, X., Novak, A. M., Eizenga, J. M., Markello, C., Sibbesen, J. A., Hickey, G., Chang, P.-C., Carroll, A., Gupta, N., Gabriel, S., Blackwell, T. W., Ratan, A., Taylor, K. D., Rich, S. S., Rotter, J. I., Haussler, D., Garrison, E. & Paten, B.
Science 374 (2021).
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Publicações
DeepNull models non-linear covariate effects to improve phenotypic prediction and association powerMcCaw, Z. R., Colthurst, T., Yun, T., Furlotte, N. A., Carroll, A., Alipanahi, B., McLean, C. Y. & Hormozdiari, F.
Nat. Commun. 13, 241 (2022).
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Publicações
A population-specific reference panel for improved genotype imputation in African AmericansO’Connell, J., Yun, T., Moreno, M., Li, H., Litterman, N., Kolesnikov, A., Noblin, E., Chang, P.-C.,Shastri, A., Dorfman, E. H., Shringarpure, S., Auton, A., Carroll, A. & McLean, C. Y.
Communications Biology 4, 1–9 (2021).
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Publicações
Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-readsShafin, K., Pesout, T., Chang, P.-C., Nattestad, M., Kolesnikov, A., Goel, S., Baid, G., Kolmogorov, M., Eizenga, J. M., Miga, K. H., Carnevali, P., Jain, M., Carroll, A. & Paten, B.
Nat. Methods 18, 1322–1332 (2021). [readcube]
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Publicações
DeepConsensus: Gap-Aware Sequence Transformers for Sequence CorrectionBaid, G., Cook, D. E., Shafin, K., Yun, T., Llinares-López, F., Berthet, Q., Wenger, A. M., Rowell, W. J., Nattestad, M., Yang, H., Kolesnikov, A., Töpfer, A., Ammar, W., Vert, J.-P., Vaswani, A., McLean, C. Y., Chang, P.-C. & Carroll, A.
bioRxiv 2021.08.31.458403 (2021).
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Publicações
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphologyAlipanahi, B., Hormozdiari, F., Behsaz, B., Cosentino, J., McCaw, Z. R., Schorsch, E., Sculley, D., Dorfman, E. H., Foster, P. J., Peng, L. H., Phene, S., Hammel, N., Carroll, A., Khawaja, A. P. & McLean, C. Y.
Am. J. Hum. Genet. (2021).
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Publicações
Accurate, scalable cohort variant calls using DeepVariant and GLnexusYun, T., Li, H., Chang, P-C., Lin, M., Carroll, A., & McLean, C. Y.
Bioinformatics 36, 5582-5589 (2021).
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Publicações
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic RegressionYadlowsky, S., Yun, T., McLean, C. & D’Amour, A.
arXiv [stat.ML] (2021).
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Publicações
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphologyAlipanahi, B., Hormozdiari, F., Behsaz, B., Cosentino, J., McCaw, Z. R., Schorsch, E., Sculley, D., Dorfman, E. H., Phene, S., Hammel, N., Carroll, A., Khawaja, A. P. & McLean, C. Y.
arXiv [q-bio.GN] (2020).
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Publicações
GenomeWarp: an alignment-based variant coordinate transformationMcLean, C. Y., Hwang, Y., Poplin, R. & DePristo, M. A.
Bioinformatics 35, 4389–4391 (2019).
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Publicações
Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genomeWenger, 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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Publicações
A universal SNP and small-indel variant caller using deep neural networksPoplin, 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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Publicações
Deep learning of genomic variation and regulatory network dataTelenti, A., Lippert, C., Chang, P.-C. & DePristo, M.
Hum. Mol. Genet. 27, R63–R71 (2018).
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Publicações
Sequential regulatory activity prediction across chromosomes with convolutional neural networksKelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y. & Snoek, J.
Genome Res. 28, 739–750 (2018).
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Posts de blog
Joint Speech Recognition and Speaker Diarization via Sequence Transductionby Laurent El Shafey and Izhak Shafran
16-Aug-2019
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Posts de blog
How AI can improve products for people with impaired speechby Julie Cattiau
Google Keyword Blog | 7-May-2019
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Posts de blog
Understanding Medical Conversationsby Katherine Chou and Chung-Cheng Chiu
Google AI Blog | 21-Nov-2017
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Publicações
Medical Scribe: Corpus Development and Model Performance AnalysesShafran, 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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Publicações
Extracting Symptoms and their Status from Clinical ConversationsDu, 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).
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Publicações
Automatically Charting Symptoms From Patient-Physician Conversations Using Machine LearningRajkomar, A., Kannan, A., Chen, K., Vardoulakis, L., Chou, K., Cui, C., & Dean, J.
JAMA Intern. Med. 179, 836–838 (2019).
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Publicações
Joint Speech Recognition and Speaker Diarization via Sequence TransductionEl Shafey, L., Soltau, H. & Shafran, I.
Proceedings of Interspeech. arXiv [cs.CL] (2019).
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Publicações
Learning to Infer Entities, Properties and their Relations from Clinical ConversationsDu, N., Wang, M., Tran, L., Li, G. & Shafran, I.
Proc. Empirical Methods in Natural Language Processing. arXiv [cs.CL] (2019).
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Publicações
Speech recognition for medical conversationsChiu, 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).
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Posts de blog
Multi-task Prediction of Organ Dysfunction in ICUsby Subhrajit Roy & Diana Mincu
Google AI Blog | 22-Jul-2021
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Posts de blog
A Step Towards Protecting Patients from Medication Errorsby Kathryn Rough & Alvin Rajkomar
Google AI Blog | 2-Apr-2020
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Posts de blog
Expanding the Application of Deep Learning to Electronic Health Recordsby Alvin Rajkomar & Eyal Oren
Google AI Blog | 22-Jan-2019
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Posts de blog
Scaling Streams with Googleby Demis Hassabis & Mustafa Suleyman & Dominic King
DeepMind Blog | 13-Nov-2018
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Posts de blog
Deep Learning for Electronic Health Recordsby Alvin Rajkomar & Eyal Oren
Google AI Blog | 8-May-2018
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Posts de blog
Making Healthcare Data Work Better with Machine Learningby Patrik Sundberg & Eyal Oren
Google AI Blog | 2-Mar-2018
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Publicações
Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routingRoy, S., Mincu, D., Loreaux, E., Mottram, A., Protsyuk, I., Harris, N., Xue, Y., Schrouff, J., Montgomery, H., Connell, A., Tomasev, N., Karthikesalingam, A. & Seneviratne, M.
J. Am. Med. Inform. Assoc. (2021).
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Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health recordsTomašev, N., Harris, N., Baur, S., Mottram, A., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Magliulo, V., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Osborne, T. F., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Seneviratne, M. G., Ledsam, J. R. & Mohamed, S.
Nat. Protoc. 1–23 (2021). [readcube]
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Learning to Select Best Forecast Tasks for Clinical Outcome PredictionXue 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 PredictionsXue 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 recordsChoi 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 recordsDusenberry 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 alertsHardt 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 dataRough, 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 injuryTomašev, 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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Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissionsConnell, 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 CostsConnell 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 ProfessionalsConnell 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 NotesKemp J, Rajkomar A, & Dai AM.
arXiv [cs.LG] (2019).
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Federated and Differentially Private Learning for Electronic Health RecordsPfohl SR, Dai AM, & Heller K.
arXiv [cs.LG] (2019).
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Deep Physiological State Space Model for Clinical ForecastingXue Y, Zhou D, Du N, Dai AM, Xu Z, Zhang K,& Cui C.
arXiv [cs.LG] (2019).
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Modelling EHR timeseries by restricting feature interactionZhang K, Xue Y, Flores G, Rajkomar A, Cui C, & Dai AM.
arXiv [cs.LG] (2019).
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Scalable and accurate deep learning with electronic health recordsRajkomar 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).
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Expanding research on digital wellbeingby Nicholas Allen
Google Keyword Blog | 23-May-2022
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Posts de blog
Advancing health research with Google Health Studiesby Jon Morgan & Paul Eastham
by Jon Morgan & Paul Eastham
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Posts de blog
How AI could predict sight-threatening eye conditionsby Terry Spitz & Jim Winkens
Google Keyword Blog | 18-May-2020
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Posts de blog
Using AI to predict retinal disease progressionby 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 eyeby Akinori Mitani
Blog The Keyword do Google | 28-jan-2020
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Assessing Cardiovascular Risk Factors with Computer Visionby Lily Peng
Google AI Blog | 2-Feb-2018
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Retinal fundus photographs capture hemoglobin loss after blood donationMitani, A., Traynis, I., Singh, P., Corrado, G. S., Webster, D. R., Peng, L. H., Varadarajan, A. V., Liu, Y. & Hammel, N.
doi:10.1101/2021.12.30.21268488
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Predicting the risk of developing diabetic retinopathy using deep learningBora, 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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Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learningMoraes, 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 TranslationNarayanaswamy, 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 learningYim, 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 learningVaradarajan, 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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Publicações
Detection of anaemia from retinal fundus images via deep learningMitani, 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 LearningBabenko, B., Balasubramanian, S., Blumer, K. E., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N. & Varadarajan, A. V.
arXiv [cs.CV] (2019).
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Publicações
Deep Learning for Predicting Refractive Error From Retinal Fundus Imagesaradarajan, 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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Publicações
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learningPoplin, 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]
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Verily and Lumea Announce Development Partnership to Advance Digital Pathology in Prostate CancerVerily Blog | 16-Mar-2022
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An International Scientific Challenge for the Diagnosis and Gleason Grading of Prostate Cancerby Po-Hsuan Cameron Chen & Maggie Demkin
Google AI Blog | 11-Feb-2022
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Posts de blog
The promise of using AI to help prostate cancer careby Po-Hsuan Cameron Chen & Yun Liu
Google Keyword Blog | 23-Sept-2021
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PAIR @ CHI 2021by People + AI Research
People + AI Research Blog | 14-May-2021
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Learning from deep learning: developing interpretable AI approaches in histopathology to predict patient prognosis and explore novel featuresby Dave Steiner, Yun Liu, Craig Mermel, Kurt Zatloukal, Heimo Muller, Markus Plass
npj Digital Medicine Blog | 19-Apr-2021
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Posts de blog
Defense Innovation Unit Selects Google Cloud to Help U.S. Military Health System with Predictive Cancer DiagnosesGoogle Cloud Blog | 2-Sep-2020
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Posts de blog
Using AI to identify the aggressiveness of prostate cancerby Kunal Nagpal & Craig Mermel
Google Keyword Blog | 23-Jul-2020
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Posts de blog
Generating Diverse Synthetic Medical Image Data for Training Machine Learning Modelsby Timo Kohlberger & Yuan Liu
Google AI Blog | 19-Feb-2020
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Posts de blog
Building SMILY, a Human-Centric, Similar-Image Search Tool for Pathologyby Narayan Hedge & Carrie Cai
Google AI Blog | 19-July-2019
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Posts de blog
Improved Grading of Prostate Cancer Using Deep Learningby Martin Stumpe & Craig Mermel
Google AI Blog | 16-Nov-2018
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Posts de blog
Applying Deep Learning to Metastatic Breast Cancer Detectionby Martin Stumpe & Craig Mermel
Google AI Blog | 12-Oct-2018
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Posts de blog
An Augmented Reality Microscope for Cancer Detectionby Martin Stumpe & Craig Mermel
Google AI Blog | 16-Apr-2018
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Posts de blog
Assisting Pathologists in Detecting Cancer with Deep Learningby Martin Stumpe & Lily Peng
Google AI Blog | 3-Mar-2017
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Publicações
Deep learning models for histologic grading of breast cancer and association with disease prognosisJaroensri, R., Wulczyn, E., Hegde, N., Brown, T., Flament-Auvigne, I., Tan, F., Cai, Y., Nagpal, K., Rakha, E. A., Dabbs, D. J., Olson, N., Wren, J. H., Thompson, E. E., Seetao, E., Robinson, C., Miao, M., Beckers, F., Corrado, G. S., Peng, L. H., Mermel, C. H., Liu, Y., Steiner, D. F. & Chen, P.-H. C.
npj Breast Cancer 8, 1–12 (2022).
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Publicações
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challengeBulten, W., Kartasalo, K., Chen, P.-H. C., Ström, P., Pinckaers, H., Nagpal, K., Cai, Y., Steiner, D. F., van Boven, H., Vink, R., Hulsbergen-van de Kaa, C., van der Laak, J., Amin, M. B., Evans, A. J., van der Kwast, T., Allan, R., Humphrey, P. A., Grönberg, H., Samaratunga, H., Delahunt, B., Tsuzuki, T., Häkkinen, T., Egevad, L., Demkin, M., Dane, S., Tan, F., Valkonen, M., Corrado, G. S., Peng, L., Mermel, C. H., Ruusuvuori, P., Litjens, G. & Eklund, M.
Nat. Med. 1–10 (2022).
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Publicações
Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology imagesSadhwani, A., Chang, H.-W., Behrooz, A., Brown, T., Auvigne-Flament, I., Patel, H., Findlater, R., Velez, V., Tan, F., Tekiela, K., Wulczyn, E., Yi, E. S., Mermel, C. H., Hanks, D., Chen, P.-H. C., Kulig, K., Batenchuk, C., Steiner, D. F. & Cimermancic, P.
Sci. Rep. 11, 1–11 (2021).
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Publicações
Determining breast cancer biomarker status and associated morphological features using deep learningGamble, P., Jaroensri, R., Wang, H., Tan, F., Moran, M., Brown, T., Flament-Auvigne, I., Rakha, E. A., Toss, M., Dabbs, D. J., Regitnig, P., Olson, N., Wren, J. H., Robinson, C., Corrado, G. S., Peng, L. H., Liu, Y., Mermel, C. H., Steiner, D. F. & Chen, P.-H. C.
Communications Medicine 1, 1–12 (2021).
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Publicações
Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason gradingWulczyn, E., Nagpal, K., Symonds, M., Moran, M., Plass, M., Reihs, R., Nader, F., Tan, F., Cai, Y., Brown, T., Flament-Auvigne, I., Amin, M. B., Stumpe, M. C., Müller, H., Regitnig, P., Holzinger, A., Corrado, G. S., Peng, L. H., Chen, P.-H. C., Steiner, D. F., Zatloukal, K., Liu, Y. & Mermel, C. H.
Communications Medicine 1, 1–8 (2021).
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Publicações
Onboarding Materials as Boundary Objects for Developing AI AssistantsCai, C.J., Steiner, D., Wilcox, L., Terry, M. and Winter, S.
Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, ACM (2021).
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Publicações
Interpretable survival prediction for colorectal cancer using deep learningWulczyn, E., Steiner, D. F., Moran, M., Plass, M., Reihs, R., Tan, F., Flament-Auvigne, I., Brown, T., Regitnig, P., Chen, P.-H. C., Hegde, N., Sadhwani, A., MacDonald, R., Ayalew, B., Corrado, G. S., Peng, L. H., Tse, D., Müller, H., Xu, Z., Liu, Y., Stumpe, M. C., Zatloukal, K. & Mermel, C. H.
npj Digital Medicine 4, 1–13 (2021).
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Publicações
Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate BiopsiesSteiner, 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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Publicações
Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy SpecimensNagpal, 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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Publicações
Deep learning-based survival prediction for multiple cancer types using histopathology imagesWulczyn, 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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Publicações
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer DetectionKohlberger, 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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Publicações
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosisChen, 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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Publicações
Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for PathologistsLiu, 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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Publicações
Similar image search for histopathology: SMILYHegde, 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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Publicações
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-MakingCai, C.J., Winter, S., Steiner, D., Wilcox, L. and Terry, M.
Proceedings of the ACM on Human-computer Interaction, 3(CSCW), pp.1-24 (2019)
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Publicações
Human-centered tools for coping with imperfect algorithms during medical decision-makingCai, C.J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G.S., Stumpe, M.C. and Terry, M.
In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14) (2019).
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Publicações
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancerNagpal, 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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Publicações
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast CancerSteiner, 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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Publicações
Detecting cancer metastases on gigapixel pathology imagesLiu, 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).
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Posts de blog
New Insights into Human Mobility with Privacy Preserving Aggregationby Adam Sadilek & Xerxes Dotiwalla
Google AI Blog | 12-Nov-2019
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Publicações
An evaluation of Internet searches as a marker of trends in population mental health in the USVaidyanathan, U., Sun, Y., Shekel, T., Chou, K., Galea, S., Gabrilovich, E. & Wellenius, G. A.
Sci. Rep. 12, 8946 (2022). [readcube]
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Publicações
COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus diseaseWahltinez, O., Cheung, A., Alcantara, R., Cheung, D., Daswani, M., Erlinger, A., Lee, M., Yawalkar, P., Lê, P., Navarro, O. P., Brenner, M. P. & Murphy, K.
Sci Data 9, 162 (2022).
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Publicações
Google COVID-19 Vaccination Search Insights: Anonymization Process DescriptionBavadekar, S., Boulanger, A., Davis, J., Desfontaines, D., Gabrilovich, E., Gadepalli, K., Ghazi, B., Griffith, T., Gupta, J., Kamath, C., Kraft, D., Kumar, R., Kumok, A., Mayer, Y., Manurangsi, P., Patankar, A., Perera, I. M., Scott, C., Shekel, T., Miller, B., Smith, K., Stanton, C., Sun, M., Young, M. & Wellenius, G.
arXiv [cs.CR] (2021).
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Publicações
Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: Insights from Spring 2020Woskie, L. R., Hennessy, J., Espinosa, V., Tsai, T. C., Vispute, S., Jacobson, B. H., Cattuto, C., Gauvin, L., Tizzoni, M., Fabrikant, A., Gadepalli, K., Boulanger, A., Pearce, A., Kamath, C., Schlosberg, A., Stanton, C., Bavadekar, S., Abueg, M., Hogue, M., Oplinger, A., Chou, K., Corrado, G., Shekel, T., Jha, A. K., Wellenius, G. A. & Gabrilovich, E.
PLoS One 16, e0253071 (2021).
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Publicações
Impacts of social distancing policies on mobility and COVID-19 case growth in the USWellenius, G. A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T. C., Hennessy, J., Dai, A., Williams, B., Gadepalli, K., Boulanger, A., Pearce, A., Kamath, C., Schlosberg, A., Bendebury, C., Mandayam, C., Stanton, C., Bavadekar, S., Pluntke, C., Desfontaines, D., Jacobson, B. H., Armstrong, Z., Gipson, B., Wilson, R., Widdowson, A., Chou, K., Oplinger, A., Shekel, T., Jha, A. K. & Gabrilovich, E.
Nat. Commun. 12, 3118 (2021).
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Publicações
Forecasting influenza activity using machine-learned mobility mapVenkatramanan, S., Sadilek, A., Fadikar, A., Barrett, C. L., Biggerstaff, M., Chen, J., Dotiwalla, X., Eastham, P., Gipson, B., Higdon, D., Kucuktunc, O., Lieber, A., Lewis, B. L., Reynolds, Z., Vullikanti, A. K., Wang, L. & Marathe, M.
Nat. Commun. 12, 726 (2021).
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Global maps of travel time to healthcare facilitiesWeiss, 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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Publicações
Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington stateAbueg, 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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Publicações
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).
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Publicações
Impacts of State-Level Policies on Social Distancing in the United States Using Aggregated Mobility Data during the COVID-19 PandemicWellenius, 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).
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Publicações
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).
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Publicações
Assessing the impact of coordinated COVID-19 exit strategies across EuropeRuktanonchai, N. W., Floyd, J. R., Lai, S., Ruktanonchai, C. W., Sadilek, A., Rente-Lourenco, P., Ben, X., Carioli, A., Gwinn, J., Steele, J. E., Prosper, O., Schneider, A., Oplinger, A., Eastham, P. & Tatem, A. J.
Science 369, 1465–1470 (2020).
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Publicações
Lymelight: forecasting Lyme disease risk using web search dataSadilek, A., Hswen, Y., Bavadekar, S., Shekel, T., Brownstein, J. S. & Gabrilovich, E.
npj Digital Medicine 3, 1–12 (2020).
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Publicações
Hierarchical organization of urban mobility and its connection with city livabilityBassolas, 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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Publicações
Machine-learned epidemiology: real-time detection of foodborne illness at scaleSadilek, A., Caty, S., DiPrete, L., Mansour, R., Schenk Jr., T., Bergtholdt, M., Jha, A., Ramaswami P., & Gabrilovich E.
npj Digital Med 1, 36 (2018).
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Posts de blog
Simplified Transfer Learning for Chest Radiography Model Developmentby Akib Uddin & Andrew Sellergren
Google AI Blog | 19-Jul-2022
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Posts de blog
Mammography collaboration in JapanGoogle Japan Blog | 25-Nov-2021
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Posts de blog
Detecção de raios-X anormais de tórax com o aprendizado profundoby Zaid Nabulsi & Po-Hsuan Cameron Chen
Google AI Blog | 1-Sep-2021
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Posts de blog
Tackling tuberculosis screening with AIby Rory Pilgrim & Shruthi Prabhakara
Google Keyword Blog | 18-May-2021
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Posts de blog
Using artificial intelligence in breast cancer screeningby Sunny Jansen & Krish Eswaran
Google Keyword Blog | 25-Feb-2021
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Posts de blog
Exploring AI for radiotherapy planning with Mayo Clinicby Cian Hughes
Google Keyword Blog | 29-Oct-2020
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Posts de blog
Using AI to improve breast cancer screeningby Shravya Shetty & Daniel Tse
Google Keyword Blog | 1-Jan-2020
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Posts de blog
Developing Deep Learning Models for Chest X-rays with Adjudicated Image Labelsby Dave Steiner & Shravya Shetty
Google AI Blog | 3-Dec-2019
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Posts de blog
A promising step forward for predicting lung cancerby Shravya Shetty
Google Keyword Blog | 20-May-2019
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Publicações
A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessmentGomes, R. G., Vwalika, B., Lee, C., Willis, A., Sieniek, M., Price, J. T., Chen, C., Kasaro, M. P., Taylor, J. A., Stringer, E. M., McKinney, S. M., Sindano, N., Dahl, G. E., Goodnight, W., Gilmer, J., Chi, B. H., Lau, C., Spitz, T., Saensuksopa, T., Liu, K., Tiyasirichokchai, T., Wong, J., Pilgrim, R., Uddin, A., Corrado, G., Peng, L., Chou, K., Tse, D., Stringer, J. S. A. & Shetty, S.
Communications Medicine 2, 1–9 (2022).
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Publicações
Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of RadiologistsKazemzadeh, S., Yu, J., Jamshy, S., Pilgrim, R., Nabulsi, Z., Chen, C., Beladia, N., Lau, C., McKinney, S. M., Hughes, T., Kiraly, A. P., Kalidindi, S. R., Muyoyeta, M., Malemela, J., Shih, T., Corrado, G. S., Peng, L., Chou, K., Chen, P.-H. C., Liu, Y., Eswaran, K., Tse, D., Shetty, S. & Prabhakara, S.
Radiology 212213 (2022).
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Publicações
Simplified Transfer Learning for Chest Radiography Models Using Less DataSellergren, A. B., Chen, C., Nabulsi, Z., Li, Y., Maschinot, A., Sarna, A., Huang, J., Lau, C., Kalidindi, S. R., Etemadi, M., Garcia-Vicente, F., Melnick, D., Liu, Y., Eswaran, K., Tse, D., Beladia, N., Krishnan, D. & Shetty, S.
Radiology 212482 (2022).
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Publicações
Study Design: Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planningAnand, A., Beltran, C. J., Brooke, M. D., Buroker, J. R., DeWees, T. A., Foote, R. L., Foss, O. R., Hughes, C. O., Hunzeker, A. E., John Lucido, J., Morigami, M., Moseley, D. J., Pafundi, D. H., Patel, S. H., Patel, Y., Ridgway, A. K., Tryggestad, E. J., Wilson, M. Z., Xi, L. & Zverovitch, A.
medRxiv 2021.12.07.21266421 (2021).
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Publicações
Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19Nabulsi, Z., Sellergren, A., Jamshy, S., Lau, C., Santos, E., Kiraly, A. P., Ye, W., Yang, J., Pilgrim, R., Kazemzadeh, S., Yu, J., Kalidindi, S. R., Etemadi, M., Garcia-Vicente, F., Melnick, D., Corrado, G. S., Peng, L., Eswaran, K., Tse, D., Beladia, N., Liu, Y., Chen, P.-H. C. & Shetty, S.
Sci. Rep. 11, 1–15 (2021).
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Publicações
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation StudyNikolov, S., Blackwell, S., Zverovitch, A., Mendes, R., Livne, M., De Fauw, J., Patel, Y., Meyer, C., Askham, H., Romera-Paredes, B., Kelly, C., Karthikesalingam, A., Chu, C., Carnell, D., Boon, C., D’Souza, D., Moinuddin, S. A., Garie, B., McQuinlan, Y., Ireland, S., Hampton, K., Fuller, K., Montgomery, H., Rees, G., Suleyman, M., Back, T., Hughes, C. O., Ledsam, J. R. & Ronneberger, O.
J. Med. Internet Res. 23, e26151 (2021).
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Publicações
Improving reference standards for validation of AI-based radiographyDuggan, G. E., Reicher, J. J., Liu, Y., Tse, D. & Shetty, S.
Br J Radiol. 94, 20210435 (2021).
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Publicações
International evaluation of an AI system for breast cancer screeningMcKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., Melnick, D., Mostofi, H., Peng, L., Reicher, J. J., Romera-Paredes, B., Sidebottom, R., Suleyman, M., Tse, D., Young, K. C., De Fauw, J. & Shetty, S.
Nature 577, 89–94 (2020). [readcube]
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Publicações
Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted EvaluationMajkowska, 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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Publicações
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomographyArdila, 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]