What challenges did Hackensack Meridian Health face before AI implementation?

Needed AI support for mammogram reading without adding clinician time. Wanted to improve diagnostic accuracy and patient outcomes. Had to meet HIPAA, Medicare, and Medicaid security and privacy requirements while modernizing workflows.

What was the impact of the AI solution?

Expected lower patient recall rates. Expected improved provider productivity through AI-supported clinical decisions.

Source

GCPExpandedProductionEvidence: Low40/100

Hackensack Meridian Health builds AI mammogram support model using Google Cloud as part of healthcare expansion

Use case typeMedical imagingUpdated Jun 13, 2026

Hackensack Meridian Health (HMH), a 17-hospital healthcare system, expanded its Google partnership from remote work and collaboration into patient care applications. HMH is building a custom AI model that analyzes 3D mammogram images to support double readings and clinical decision-making without adding clinician time. The model is being built with Google Cloud products including Cloud Healthcare API and is intended to be trained, deployed, and maintained on Google Cloud and integrated into clinical workflows.

Organization: Hackensack Meridian Health
Industry: Healthcare
Location: United States
Published: February 2022

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Hackensack Meridian Health
Provider: GCP
Maturity: Production
Linked source: Google Workspace Blog

The model is being built with Google Cloud products including Cloud Healthcare API and is intended to be trained, deployed, and maintained on Google Cloud and integrated into clinical workflows

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Clinical Imaging AI
2Diagnostic Support
3Workflow Automation

Built a custom AI model to analyze 3D mammogram images.
Used Cloud Healthcare API and other Google Cloud products to develop the model.
Planned to train, deploy, and maintain the model on Google Cloud and embed it into clinical decision workflows.

Technologies

Cloud Healthcare API Google Cloud

Expected lower patient recall rates.
Expected improved provider productivity through AI-supported clinical decisions.

Architecture

The article describes HMH building a custom mammography AI model on Google Cloud, using Cloud Healthcare API as part of the data and model-building stack, with planned training, deployment, and maintenance on Google's cloud and integration into clinical workflows.

Sources & evidence1

Evidence: Low40/100Evidence strength