Health
We are working to create new machine learning tools and discover opportunities to increase the access and accuracy of healthcare globally.
We are working to create new machine learning tools and discover opportunities to increase the access and accuracy of healthcare globally.
About the team
Transformative healthcare technologies build upon translational research to realize long-term potential. Our Health AI team embraces a model of embedded research and development where scientists work in tight integration with clinical, product, and engineering teams, collaborating with academic and clinical partners globally to conduct research into improving healthcare. Read about some of our recent efforts on the Google AI blog and Google Keyword blog.
Team focus summaries
Highlighted work
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Med-PaLMMed-PaLM is a Large Language Model fine-tuned and designed to provide high quality answers to medical questions. -
Multimodal medical AIWe are exploring a spectrum of approaches to building multimodal medical LLMs, for example through Med-PaLM, our large language model (LLM) designed to provide high quality answers to medical questions. -
Google Health research publicationsPublishing 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. -
Helping doctors address eye diseaseWe are exploring computer-aided diagnostic screening for diabetic retinopathy, one of the fastest growing causes of preventable vision loss globally, as in much of the world, there simply are not enough doctors available to support the volume of screening required for the population. -
Applications in breast cancer researchOur team is working with researchers in mammography and pathology to develop ML that might one day assist clinicians in screening and diagnosing breast cancer. -
Deep learning for electronic health recordsWe are using deep learning models to make a broad set of predictions relevant to hospitalized patients using de-identified electronic health records and discovering how models can be used to render accurate predictions.
Some of our locations
Some of our people
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Yun Liu
- General Science
- Health & Bioscience
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Naama Hammel
- Health & Bioscience
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Yuan Liu
- Data Mining and Modeling
- Machine Intelligence
- Machine Perception
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Mark Schlager
- Human-Computer Interaction and Visualization
- Health & Bioscience
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Vivek Natarajan
- Education Innovation
- Machine Intelligence
- Algorithms and Theory
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Christopher Semturs
- Human-Computer Interaction and Visualization
- Machine Intelligence
- General Science
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Alan Karthikesalingam
- Machine Intelligence
- Machine Perception
- Health & Bioscience
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Marcin Sieniek
- Distributed Systems and Parallel Computing
- Machine Intelligence
- Data Mining and Modeling
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Pi-Chuan Chang
- General Science
- Machine Intelligence
- Natural Language Processing
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Ivan Protsyuk
- Health & Bioscience
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Daniel Tse
- Machine Intelligence
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Lily Peng
- Machine Intelligence
- Health & Bioscience
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Dale Webster
- General Science
- Machine Intelligence
- Machine Perception
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Izhak Shafran
- Machine Intelligence
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Cory McLean
- General Science
- Machine Intelligence
- Health & Bioscience
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Joe Ledsam
- General Science
- Health & Bioscience
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Ayush Jain
- Algorithms and Theory
- Data Mining and Modeling
- General Science
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Kasumi Widner
- Machine Intelligence
- Machine Perception
- Health & Bioscience
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Jake Garrison
- General Science
- Hardware and Architecture
- Audio Processing
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Katherine Heller
- Machine Intelligence
- Health & Bioscience
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Diana Mincu
- Machine Intelligence
- Health & Bioscience
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Natalie Harris
- Human-Computer Interaction and Visualization
- Machine Intelligence
- Software Engineering
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Jessica Schrouff
- Machine Intelligence
- Responsible AI
- Health & Bioscience
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Jonas Kemp
- Machine Intelligence
- Natural Language Processing
- Health & Bioscience
