AI and imaging

AI-driven medical diagnostics and imaging workflow platform

A SaaS platform that moves imaging studies from upload through review and reporting, helps radiologists draft structured summaries faster and handles the billing that follows.

Our BAA offer

Built forRadiology and imaging centers

Team shapeAround 7 people: architect, backend and frontend engineers, DevOps, QA, AI engineer

TimelineAbout 8 months

The problem

Imaging centers handle very large files, strict access rules and a reporting step that is mostly typing. They also bill patients and insurers through several channels.

How we approached it

We built a secure study pipeline on AWS, a review workspace with annotations and peer comments, and a generative AI step that proposes a structured draft for the radiologist to edit and sign. The AI never finalizes a report. A clinician always does.

What we built

DICOM and image file management on AWS EC2 and S3 with encrypted storage
Generative AI integration that drafts structured diagnostic summaries for radiologist review
Peer review, second reads and annotation sharing between clinicians
Encrypted clinician messaging attached to a study
Multi-provider payment and subscription billing, including patient co-pays and insurance reimbursement tracking
Turnaround time dashboards for center managers

Architecture

How the pieces fit together

1Modalities and uploads

2Secure study storage

3Review workspace and AI draft

4Billing and reporting

Study intakeResumable, checksum verified uploads into private buckets with per study access policies and lifecycle rules.

Viewer and reviewStudies stream to the browser through short lived signed URLs. Annotations are stored as structured data linked to the study.

AI draftingPrompts use only the minimum necessary data, run through a vendor under a BAA, and the output is stored as a draft with the model version recorded.

BillingA payments module handles several processors and reconciles co-pays and reimbursements.

Safeguards built in

Security and compliance controls

Private buckets and signed URLs
AI vendor under a BAA and no training on PHI
Model version and edits recorded with each report
Encrypted messaging
Access logs per study

What changed

Outcomes for the operator

Radiologists start from a structured draft instead of a blank page and stay in control of the final report.
Peer review happens inside the study, with a record of every comment.
Managers see turnaround and billing status without chasing spreadsheets.

Technology

LaravelAWS EC2AWS S3DICOM toolingMySQLGenerative AI APIStripe

Integrations

Modality and PACS exportsPayment processorsInsurance clearinghouseGenerative AI provider

Representative engagement. Client details and internal figures are withheld under NDA. Compliance depends on how a system is operated as well as how it is built, and your own counsel decides what applies to you.