What challenges did Karkinos Healthcare face before AI implementation?

Limited access and awareness delayed cancer diagnosis and treatment, especially for underinsured populations in India. Need for scalable, flexible, and secure infrastructure to support large heterogeneous data and provide quality cancer care remotely. Requirement for multi-language and environment-customized risk assessments, and large genomics data processing. Optimization of resource allocation and cost control for sustainable platform operation.

What was the impact of the AI solution?

Supported early cancer detection for over 400,000 Indians with scalable risk assessments and screening surveys exceeding 5,000 per day. Processed more than 10,000 genomics sequences annually producing 1.5 terabytes of data daily. Enabled oncology services access through 70 partner clinics across remote and underserved regions. Provided upskilling for doctors to deliver standardized oncological services. Improved operational resilience and reduced infrastructure management overhead through fully managed Google Cloud services.

Source

GCPProductionEvidence: Medium55/100

Karkinos Healthcare Deploys Scalable AI Platform for Early Cancer Detection Using Google Cloud

Use case typeMedical imagingUpdated Jun 13, 2026

Karkinos Healthcare developed a scalable, cloud-native oncology platform focused on early cancer detection and care for underinsured populations across India. The platform manages large genomic datasets, conducts low-cost, population-scale cancer risk assessments customized for diverse local languages and environments, and integrates partner clinics. The system processes over 10,000 genomics sequences yearly and serves over 400,000 users, enabling early cancer diagnosis with AI-powered machine learning for imaging analysis and NLP for clinical notes. Technology choices include Google Kubernetes Engine for automatic scaling, Cloud Healthcare API, Cloud Load Balancing, Google Cloud Armor for security, and Google Cloud Storage. Partner Persistent Systems supported infrastructure design and billing optimization. The solution helped onboard 70 partner clinics, facilitated patient access in remote regions, and provided upskilling for doctors to improve oncological care.

Organization: Karkinos Healthcare
Industry: Healthcare
Location: India
Published: May 2026

Reported outcomes

Strategic outcomes

Market & geographic expansionExpanded oncology access to remote regionsNew product / capabilityBuilt scalable AI cancer screening platformCustomer experience & trustImproved access to earlier cancer detectionBetter decisions & insightEnabled context-aware risk assessments

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

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

Improved operational resilience and reduced infrastructure management overhead through fully managed Google Cloud services

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Natural Language Processing
2Machine Learning for Medical Imaging
3Cloud AI Platform

Limited access and awareness delayed cancer diagnosis and treatment, especially for underinsured populations in India.
Need for scalable, flexible, and secure infrastructure to support large heterogeneous data and provide quality cancer care remotely.
Requirement for multi-language and environment-customized risk assessments, and large genomics data processing.
Optimization of resource allocation and cost control for sustainable platform operation.

Built a cloud-native platform leveraging Google Cloud technologies: Google Kubernetes Engine, Cloud Healthcare API, Cloud Load Balancing, Google Cloud Armor, and Cloud Storage.
Developed AI-powered machine learning models for radiology and histopathology image analysis and built NLP models for extracting structured data from oncology clinical notes.
Implemented scalable automated risk assessment workflows adaptable to different local contexts and languages.
Partnered with Persistent Systems for digital engineering and enterprise modernization support.
Onboarded over 70 partner clinics with tools to manage patient records and oncology protocols closer to patients' homes, including in remote areas.

Technologies

Google Kubernetes Engine Cloud Healthcare API Cloud Load Balancing Google Cloud Armor Google Cloud Storage Machine learning Natural Language Processing

Supported early cancer detection for over 400,000 Indians with scalable risk assessments and screening surveys exceeding 5,000 per day.
Processed more than 10,000 genomics sequences annually producing 1.5 terabytes of data daily.
Enabled oncology services access through 70 partner clinics across remote and underserved regions.
Provided upskilling for doctors to deliver standardized oncological services.
Improved operational resilience and reduced infrastructure management overhead through fully managed Google Cloud services.

Architecture

The architecture is designed on Google Kubernetes Engine to achieve automated scaling and high availability. It incorporates Cloud Healthcare API for data interfacing, Cloud Load Balancing for traffic distribution, and Google Cloud Armor for protection against DDoS and other attacks. Storage for large imaging and genomics data is handled through Google Cloud Storage. The AI models include automated ML for imaging and NLP for clinical notes.

Sources & evidence1

Evidence: Medium55/100Evidence strength