What challenges did ROKIT Healthcare face before AI implementation?

Personalize treatment methods to match each patient’s biological characteristics for wound healing and organ regeneration. Enable easy, accurate creation of customized regenerative patches in hospital operating rooms. Reduce manual efforts and technical complexity in ML model training and deployment for global medical access.

What was the impact of the AI solution?

Reduced model training and deployment time and efforts by 90%, focusing developer effort on model development. Delivered same level of medical service anywhere with only a tablet and 3D printer, democratizing access to regenerative treatments. Expanded treatment capabilities from skin to cartilage regeneration through additional Vertex AI models. Established a reliable, automated global AI-powered regenerative medicine platform.

Rank#199of 3,449

Source

GCPProductionEvidence: Medium65/100

ROKIT Healthcare builds global AI-driven regenerative medicine platform with Vertex AI

Use case typeMedical imagingUpdated Jun 13, 2026

ROKIT Healthcare personalizes and accelerates wound healing and organ regeneration treatments using AI and MLOps for global scalability. ROKIT developed ML models with Vertex AI to analyze 3D wound data from smartphone sensors, create personalized 3D bio-ink patches for skin and cartilage regeneration. They deployed an MLOps environment on Google Kubernetes Engine for seamless machine learning lifecycle management and global deployment.

Organization: ROKIT Healthcare
Industry: Healthcare
Location: South Korea
Published: May 2026

Reported outcomes

−90%

timeTime & speed

Strategic outcomes

New product / capabilityPersonalized regenerative patch creationSpeed & agilityAutomated model training and deploymentMarket & geographic expansionEnabled global medical accessNew product / capabilityExpanded to cartilage regeneration

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 90% decrease

Google Cloud Customer StoriesMay 9, 2026Customer storyInferred claimMedium evidence strength

Reduced model training and deployment time and efforts by 90%, focusing developer effort on model development.

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

Customer: ROKIT Healthcare
Provider: GCP
Maturity: Production
Linked source: Google Cloud Customer Stories

They deployed an MLOps environment on Google Kubernetes Engine for seamless machine learning lifecycle management and global deployment

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Personalized medicine
2MLOps
3Medical imaging

Personalize treatment methods to match each patient’s biological characteristics for wound healing and organ regeneration.
Enable easy, accurate creation of customized regenerative patches in hospital operating rooms.
Reduce manual efforts and technical complexity in ML model training and deployment for global medical access.

Leveraged Google Cloud services including Vertex AI and Google Kubernetes Engine to build an integrated AI pipeline.
Developed machine learning models that interpret 3D wound data via smartphone sensors and generate 3D printing data for bio patches.
Created an MLOps environment allowing automation of training, deployment, and operation, reducing effort by 90%.
Enabled data flow and AI processing entirely in the cloud for real-time, seamless medical application globally.

Technologies

Vertex AI Google Kubernetes Engine

Reduced model training and deployment time and efforts by 90%, focusing developer effort on model development.
Delivered same level of medical service anywhere with only a tablet and 3D printer, democratizing access to regenerative treatments.
Expanded treatment capabilities from skin to cartilage regeneration through additional Vertex AI models.
Established a reliable, automated global AI-powered regenerative medicine platform.

Architecture

The solution uses Google Cloud's Vertex AI for ML model development and inference, integrated with Google Kubernetes Engine for MLOps lifecycle management and deployment. Data from smartphone sensors is processed in Vertex AI to produce precise 3D printing data for regenerative bio patches. The MLOps environment automates training, deployment, and operation workflows.

Sources & evidence1

Evidence: Medium65/100Evidence strength