Live Platform — India Deployment

Radiology read.
Any scan. Any city.

An AI-assisted cloud imaging platform built for the Indian diagnostic network — where radiologist scarcity is structural, turnaround time is clinical, and the infrastructure was not designed for what is being asked of it.

1:100k
Radiologist ratio in India
Global benchmark is 1:10,000
15k
Radiologists for 1.4B
Nearly half practise abroad
60m
Routine TAT target
Under 30 min for emergencies
$7.7b
Ayushman Bharat investment
More machines, same shortage

The Problem We're Solving

The gap is not closing.
It is widening.

India is placing scanners in district hospitals that previously had no imaging capability. The radiologists to read them are not following. A conventional PACS moves the scan from film to screen. It does not move the radiologist to the scan.

01 — Supply

Radiologists concentrated in metros

Tier 2 and Tier 3 cities — where new diagnostic infrastructure is being placed — are largely on their own. No routing intelligence in a legacy system changes that.

02 — Demand

Policy is accelerating the gap

The Ayushman Bharat Health Infrastructure Mission is systematically expanding scan capacity. More studies, same shortage. The gap widens by design.

03 — Technology

Legacy PACS was not built for this

On-premises servers, no dynamic matching, no AI assist, no subspecialist routing. A tool for a world where radiologists and scanners were in the same room.

04 — Blank Page

AI should reduce burden, not add to it

An overloaded radiologist opening a complex MRI series to a blank report template wastes the most constrained resource in the system — clinical attention.

How It Works

From scan upload to signed report

A living network — not a storage system. Every step orchestrated, audited, and AI-assisted.

01

Study Ingest

Diagnostic centre uploads DICOM study. Data routes within Indian Azure regions. No cross-border transfer.

02

AI Draft

Structured findings generated before the radiologist opens the viewer — flags, laterality, critical findings surfaced.

03

Smart Routing

Study matched to radiologist by modality, urgency, subspecialty, availability. Geography irrelevant.

04

Review & Sign

Radiologist judges, amends, signs. AI contribution explicitly marked. Liability structurally unambiguous.

05

Delivery

Timestamped, audited, priced transparently. Routine under 60 min. Emergencies under 30.

Platform Capabilities

What it actually does

Built for the India that exists — not the one that might exist in a decade.

AI-Assisted Reporting

Medical-domain AI generates structured draft reports from multi-series DICOM studies. 2D and volumetric work handled by purpose-tuned models.

Live

Dynamic Radiologist Matching

Credentialed radiologists matched to studies by modality, subspecialty, urgency, and real-time availability. Marketplace logic, not a contact list.

Live

SLA-Backed Turnaround

Sixty minutes for routine. Thirty for emergencies. Operational commitments backed by orchestration architecture, not aspirational targets.

Live

Data Residency

All DICOM data stays within Indian Azure regions — Central India and South India. No cross-border transfer at any stage of the pipeline.

Compliant

Transparent Pricing

Diagnostic centres see pricing before they submit a study. No surprises. No opaque billing. Settlement built into the marketplace layer.

Live

Full Audit Trail

Every touchpoint logged. Every report timestamped. Every AI contribution explicitly marked as AI-generated and radiologist-reviewed.

Compliant

Who This Is For

Three readers. One platform.

The clinical problem, the operational problem, and the infrastructure problem are the same problem — viewed from different seats.

Diagnostic Centre Owner

The Operator

  • Scans accumulating faster than reading capacity
  • No subspecialist on panel for complex studies
  • TAT commitments you cannot always keep
  • Need revenue certainty, not another SLA negotiation
Radiologist

The Clinician

  • Credentialed, available, not location-constrained
  • Read across centres without operational overhead
  • AI assistance that understands clinical imaging
  • Transparent, on-time payment settlement
Health-Tech CIO

The Evaluator

  • Evaluating AI-assist for a diagnostic network
  • Data residency compliance without cloud compromise
  • Understand what the infrastructure actually costs
  • Tired of being sold demos that don't survive production

Regulatory Architecture

Built in phases.
Designed for compliance.

Revenue-generating operations run now under the established teleradiology model. The AI-assist layer is deployed as decision-support — a classification that is currently defensible.

Formal SaMD licensing for the AI components is in process. We designed around it clearly, so the transition does not require rebuilding anything.

Azure Health Data Services
Indian regional data residency — Central & South India
Teleradiology framework compliant
Revenue operations live under established regulatory model
SaMD licensing in process
AI components structured for CDSCO transition — no rebuild required
AI attribution explicit
AI assists. Radiologist signs. Structurally unambiguous.

Work With Astraios

This is a live platform.
Not a proof of concept.

We are selectively onboarding diagnostic centres and radiologist networks for the next phase. If you are evaluating this seriously — not browsing, evaluating — talk to us.