Normalized claim
Accuracy: 5-10 x increase
Accelerated development of predictive machine learning models for Alzheimer's detection by 5-10x with improved accuracy measured by AUC increase from 0.7 to 0.8.
GCPEvidence: Medium50/100
Foundation for Precision Medicine Uses Google Cloud and BigQuery to Accelerate Alzheimer’s Detection
Use case typeAI development platformUpdated Jun 13, 2026
The Foundation for Precision Medicine aimed to detect Alzheimer's disease early, months or years before symptoms manifest, to enable timely treatment and alter disease trajectory. They migrated data analysis and machine learning model development to Google Cloud, leveraging BigQuery for fast processing of large electronic health record datasets and virtual machines for scalable compute power. This enabled faster, more accurate machine learning algorithm development and collaborative research, reclaiming significant researcher time for scientific discovery.
- Organization
- Foundation for Precision Medicine
- Industry
- Healthcare
- Location
- United States
- Published
- May 2026
Reported outcomes
5-10x
accuracyQuality & accuracy
70%time
Strategic outcomes
New product / capabilityDeveloped faster, more accurate Alzheimer’s detection modelsSpeed & agilityAccelerated predictive model developmentEmployee experienceReclaimed time for scientific discoveryCustomer experience & trustImproved data accessibility and compliance
Catalog median for quality & accuracy deployments: +41% across 63 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 70%
Reclaimed 70% of data team’s time from data engineering to focus on scientific discovery.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Foundation for Precision Medicine
- Provider
- GCP
- Maturity
- Unknown
- Linked source
- Google Cloud Customer Stories
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 3
- 1Machine Learning
- 2Medical Research
- 3Healthcare AI
- Early detection of Alzheimer's disease from large, complex health datasets to improve treatment outcomes and reduce healthcare system costs.
- Limited compute and data infrastructure initially restricted analysis scale and speed, delaying algorithm development and accuracy improvements.
- Use of Google Cloud services including BigQuery for large-scale dataset storage and analysis, BigQuery ML for machine learning model development, Compute Engine virtual machines with CPU and GPU support for model training, and Looker Studio for data visualization.
- Migration from local servers to Google Cloud improved data accessibility, compliance, and enabled collaborative research with global partners, accelerating scientific discovery.
- Accelerated development of predictive machine learning models for Alzheimer's detection by 5-10x with improved accuracy measured by AUC increase from 0.7 to 0.8.
- Reclaimed 70% of data team’s time from data engineering to focus on scientific discovery.
- Enabled faster clinical research collaboration and is enabling plans for deploying a scalable Alzheimer's detection mobile app with Google Cloud backend.
- Supports ongoing research into other brain diseases and pharmacogenomics.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 10, 2026Publisher: Google Cloud Customer StoriesEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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