Gleamer partners with Google Cloud to revolutionize radiology with AI

Use case typeMedical imagingUpdated Jun 13, 2026

Gleamer developed AI solutions to automate and enhance radiological image analysis globally. Their platform leverages Google Cloud's scalable infrastructure, including BigQuery, GKE, Datastore, and Vertex AI with Med-PaLM and Gemini AI models for medical imaging diagnostics. The solution supports over 2,500 institutions across 45 countries, analyzing 35 million exams annually and improving lesion detection accuracy by up to 30%. The AI powered automated report generation reduces radiologist workloads and improves diagnostic quality and accessibility, particularly in underserved regions.

Organization
Gleamer
Industry
Healthcare
Location
France

Reported outcomes

+30%

accuracyQuality & accuracy

Strategic outcomes

Market & geographic expansionExpanded into international medical marketsNew product / capabilityBuilt an AI radiology diagnostic platformNew product / capabilityAutomated radiology report generationRisk & complianceStrengthened medical data security and compliance

Catalog median for quality & accuracy deployments: +90% across 270 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

  • 1Medical imaging AI
  • 2Radiology automation
  • 3Diagnostic accuracy enhancement
Increasing demand for radiological analysis amid shortages of specialists, especially outside major medical centers.
  • Built an AI diagnostic platform on Google Cloud using Vertex AI, Med-PaLM, Gemini models, BigQuery, GKE, and Datastore.
  • Implemented automated radiology report generation with AI models to reduce manual effort and speed delivery of results.
  • Used Google Cloud's HDS certification to ensure data security and compliance for sensitive medical data.
  • Expanded AI coverage to multiple imaging modalities and international markets using Google Cloud's global infrastructure.
  • Deployed in 2,500+ institutions in 45 countries, processing over 35 million exams annually.
  • Improved lesion detection accuracy by up to 30%, matching specialist-level diagnostics.
  • Reduced workloads for radiologists, enabling focus on complex cases and expanding access to high-quality radiology care.
  • Enabled international growth and adoption, particularly in underserved medical deserts.
Architecture

Architecture uses Google Kubernetes Engine for scalable deployment, BigQuery for data analysis, Datastore for state management, and Vertex AI with Med-PaLM and Gemini for medical LLMs and diagnostics.

Sources & evidence1
Groundedness: 5/5

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