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September 5, 2025 | 
MedTech projects bring a unique level of complexity in all areas of development and delivery. In this case, we are working on a solution to train the machine to recognize malignant cells in medical scans of tissue. Ensuring the confidentiality, integrity, and availability of the data is a challenge in itself. This data isn’t just “data”, it’s the scan a doctor uses to detect a tumor, the image that helps decide a treatment plan, the file that needs to load instantly and securely for someone’s life to move forward.
Therefore, what we want to showcase for interested readers was a specific image hosting solution: design and deploy a cloud-native platform that could handle the full lifecycle of medical image ingestion, processing, storage, and retrieval-without breaking under clinical demands.
The Challenge of Cloud Hosting in MedTech Projects
High-resolution scans are streamed in from imaging devices and are going through an AI pipeline. By the time they reach the end, the output is standardized DICOM images. Consequently, those images need a hosting layer that could scale, stay secure, and integrate seamlessly with healthcare systems.
Clearly, this isn’t about “just storing files.” It is about building an environment where medical professionals could pull up scans in real time, across institutions, with zero friction. In fact, reliability, compliance, and speed aren’t nice-to-haves; they are non-negotiable.
Our Approach to Building a Cloud-Based Medical Image Platform
We leaned into Google Cloud’s healthcare-focused stack and built a platform with security, scalability, and automation at its core.
Therefore, here’s how the architecture came together:
- Google Cloud Healthcare API → Secure, HIPAA-compliant storage and management of DICOM images with native DICOMweb support.
- SLIM Viewer + Firebase Hosting → A lightweight, web-based viewer deployed globally for near-instant access by clinicians.
- CI/CD with GitHub + Cloud Build → Every commit automatically deployed, reducing downtime and human error.
- Cloud Run Jobs → Containerized DICOMWeb client running on demand to handle ingestion and background processing at scale.
- Secret Manager + IAM → Centralized and secure management of sensitive configs with strict role-based access control.
Everything was built to flex: autoscaling, serverless where possible, and designed to grow with imaging volume without sacrificing performance.
Deployment Workflow in Action
- Deploy SLIM Viewer frontend to Firebase.
- Create and populate DICOM stores through the Healthcare API.
- Configure SLIM to connect with a custom DICOMWeb endpoint.
- Spin up Cloud Run Jobs to process and prep images.
- Lock down secrets and permissions with Secret Manager and IAM.
From there, medical teams could log in, open the viewer, and instantly retrieve the scans they needed.
Results
- Real-time access: Clinicians gained instant visibility into imaging studies.
- Less infrastructure drag: Serverless design meant fewer ops headaches.
- Future-proof scaling: The system can handle surges in imaging data without rewrites.
- Compliance built in: HIPAA alignment baked into every layer.
Rapid iteration: Automated CI/CD pipelines shortened release cycles and cut deployment risk.
Why It Matters
This project wasn’t just an infrastructure exercise – it was about enabling better clinical outcomes. As a result, the faster a scan loads, the sooner a decision is made. The more reliable the platform, the more trust it earns in a clinical setting.
By building on a cloud-native, automated foundation, we delivered a solution that’s not only reliable today but designed to evolve with tomorrow’s healthcare needs.
