Health
We are working to create new machine learning tools and discover opportunities to increase the availability and accuracy of healthcare globally.
We are working to create new machine learning tools and discover opportunities to increase the availability 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 Research blog and Google Keyword blog.
Team focus summaries
In partnership with healthcare organizations globally, we’re working to develop robust new ML-enabled tools designed to assist clinicians. Drawing from diverse datasets, high-quality labels, and state-of-the-art deep learning techniques, we hope to translate the promise of ML into better care that benefits everyone.
Sequencing genomes enables clinicians to identify variants in a person’s DNA that indicate genetic disorders such as an elevated risk for breast cancer. DeepVariant is an open-source variant caller that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
We’re exploring the ways that sensors that are already built into smartphones and other devices, such as the camera and accelerometer, can also be helpful for daily health and wellness.
We are developing tools and resources for the broader public health community, epidemiologists, analysts and researchers working to understand and address the impacts of public health needs.
Highlighted work
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MedGemmaMedGemma is Google’s most capable open model for medical text and image comprehension, built on Gemma 3, and is a part of our Health AI Developer Foundations. Developers can use MedGemma to accelerate building healthcare-based AI applications. -
AMIEWe’re researching how AI systems could serve as conversational partners to patients and physicians in clinical settings, using Articulate Medical Intelligence Explorer (AMIE). AMIE is a research AI system designed to take a medical history, ask questions to help derive a differential diagnosis, and suggest appropriate investigations or treatments – all while interacting with empathy. -
Researching personalized health and wellness with AIMobile and wearable devices are another promising area where generative AI models could provide personalized insights for both healthcare and wellness. Learn more about our research towards a personal health agent, SensorLM, and more. -
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. -
Turn medical speech into actionable informationWe develop models for understanding medical speech and conversations and make them available publicly via Google Speech APIs. We are also developing next generation tools for recognizing symptoms of speech disorders from medical audio.
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
- 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