Google Cloud Healthcare United States

DeepHealth

Document Processing · Real-time Analytics

DeepHealth needed to handle rising imaging volumes, a worsening radiologist shortage, and the need to deliver faster, more consistent cancer detection without limiting access to high-quality diagnostics to major urban hospitals.

20M+ medical images streamed yearly with Cloud Healthcare API
21% increase in breast cancer detection rates
76% of lung cancers found at earlier stages
>90% of characterized nodules are accepted by radiologists without any changes

Reported impact

Business outcome

DeepHealth improved detection and reporting consistency while helping clinicians find cancers earlier, reduce manual reporting work, and scale advanced imaging workflows across a broad provider network.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

DeepHealth built DeepHealth OS entirely on Google Cloud to unify clinical AI, enterprise imaging, and imaging operations in a single radiology platform. Cloud Healthcare API streams and stores more than 20 million medical images each year with healthcare-oriented privacy controls and multi-zone replication for availability across regions. Gemini Enterprise Agent Platform routes exams to the right specialists based on qualifications and expertise, while Gemini drafts and summarizes radiology reports from dictated findings so physicians can review structured results faster. For domain-specific reasoning, DeepHealth fine-tunes MedGemma for healthcare use cases. BigQuery curates training cohorts and powers performance analytics, and Looker sits on top of the stack to surface dashboards and outcome insights to clinical and operational users.

Data flow

Imaging centers stream scans into Cloud Healthcare API, where DeepHealth OS makes them available to radiology workflows. Gemini Enterprise Agent Platform routes studies to the right specialists, Gemini and MedGemma assist with report drafting and clinical reasoning, and BigQuery plus Looker expose cohort, quality, and performance analytics across the network.

Solution architecture

6 components · 5 layers
  1. Ingest
    • Cloud Healthcare API Streams and stores imaging data for DeepHealth OS with healthcare-oriented interoperability, privacy, and multi-zone availability.
  2. Compute
    • Gemini Summarizes dictated radiology findings into draft reports that radiologists can quickly review and finalize.
    • MedGemma Provides medical-grade language-model reasoning that DeepHealth fine-tunes for specialized clinical AI workflows.
  3. Storage
    • BigQuery Curates model-training cohorts and stores performance analytics used by physicians and technologists.
  4. Serving
    • Looker Delivers organization-wide dashboards so non-technical users can analyze imaging performance and outcomes.
  5. Orchestration
    • Gemini Enterprise Agent Platform Routes imaging exams to the right radiology specialists and coordinates agentic workflow steps across radiology operations.

Architecture clues

  • BigQuery supports both AI model training cohorts and customer-facing analytics
  • Cloud Healthcare API uses multi-zone replication across regions
  • DeepHealth OS is built entirely on Google Cloud
  • Gemini Enterprise Agent Platform routes exams by specialist qualification and expertise
  • Looker surfaces dashboards for physicians and technologists

Evidence from the source

DeepHealth uses BigQuery to curate data cohorts for AI model training and to provide performance analytics to customers.
DeepHealth uses Gemini Enterprise Agent Platform to run agentic tooling across radiology workflows, mainly routing the right exams to the right specialists.
Gemini helps draft or summarize report content that, once finalized, referring physicians can act on.
Looker sits on top of the stack to deliver organizational dashboards across the network.
The Cloud Healthcare API handles DeepHealth's image streaming, delivering more than 20 million medical images per year.
Transparency Evidence & methodology 95/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Google Cloud

    DeepHealth customer story

    First-party
    https://cloud.google.com/customers/deephealth
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Aug 5, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Aug 4, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.