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Healthcare · Medical imaging analysis

Your radiologists are behind on scans, and the backlog is structural

Banao builds custom vision models that assist radiologists on high-volume, pattern-repetitive reads — chest X-rays, CT nodule detection, retinal screening — and integrates those models into your PACS and reporting workflow, not a parallel silo.

The goal is throughput and consistency: fewer missed findings on routine screens, faster turnaround on urgent reports, and inter-reader variation that the AI surfaces rather than hides.

What a Banao imaging AI deployment includes

A custom imaging model without PACS integration is a demo. We own the model, the integration, and the radiologist workflow — all three.

Vision models trained on your modality and anatomy

We train on your institution's labelled studies — not a generic public dataset — so the model learns your equipment's image characteristics, your patient population's demographics, and your radiologists' grading thresholds.

PACS and RIS integration, not a parallel silo

The AI output appears inside your radiologists' existing PACS viewer and feeds the RIS report template directly. No second screen, no copy-paste, no workflow detour.

Worklist prioritisation by urgency

High-acuity findings — suspected PE, tension pneumothorax, critical correlates — surface at the top of the worklist automatically, so the scan that needs a read in the next hour gets one.

Inter-reader variability flagging

When the model's confidence diverges from the draft report, it flags the discrepancy for the radiologist's review rather than staying silent. The radiologist decides; the AI keeps the record.

Radiology ops dashboard

Turnaround time by modality, finding rate by equipment, queue depth by shift — the numbers a radiology head or CMO needs to see daily, not monthly.

Compliance and audit-ready architecture

Every inference is logged with the model version, the input study, and the radiologist's final action. HIPAA, DISHA, and UAE PDPL controls are built in by default, not retrofitted.

Where this pattern is in production

Client names are shared under NDA where confidentiality applies. Metrics shown dotted (··) are being finalised in our case-study metrics pack — we publish only once the number is verified.

A GCC diagnostic imaging group

AI-assisted worklist prioritisation deployed across CT and chest X-ray queues

  • ··%reduction in critical-finding turnaround time
  • ··%worklist studies triaged without manual pre-sort
  • ··%inter-reader variability on flagged studies

A multi-site diagnostic group in the GCC was running overnight CT queues with no structured prioritisation — critical findings waited alongside routine screens. Banao built a worklist prioritisation layer that classifies urgency at study intake and surfaces critical candidates to the top of the queue, integrated directly with their existing PACS viewer. The client is named on request under NDA.

We run our own company on the AI we sell

Banao runs a ~300-person engineering company on its own AI systems before any client sees them. InterviewGod screens our own hires every week. Vikaas runs our own demand-gen pipeline end to end.

In imaging AI, that means our engineering teams have built and iterated on production vision systems under the same compliance and integration constraints we are asking your radiology department to accept. We are not describing this from the outside.

  • InterviewGodScreens Banao's own engineering hires every week.
  • VikaasRuns Banao's own demand-gen pipeline end to end.

When imaging AI is the wrong starting point

Imaging AI is expensive to validate and slow to reach clinical use if the foundation is wrong. We will tell you when to wait:

  • Too few labelled studies: a useful model typically needs thousands of labelled studies per finding class. If your institution's data is thin, the Discovery Sprint tells you whether augmentation, federation, or a different use case is the right move.
  • Regulatory pathway unclear: some imaging AI products require FDA 510(k) or CE marking before clinical use. If your use case sits in that class and the regulatory path isn't scoped, we scope it before any build — not after.
  • PACS vendor blocks integration: some PACS vendors lock their viewer and HL7 feeds. We run an integration audit in week one, and if it is genuinely blocked, we will say so rather than quote a build that cannot land.
  • Findings a radiologist must own alone: we do not build AI that replaces the radiologist's sign-off. If the use case requires autonomous diagnosis without a clinician in the loop, it is not one we will take.

How we start — prove the model before you build the integration

Imaging AI has a long history of pilots that passed a research benchmark and failed in clinical use. We start differently.

  1. AI Discovery Sprint2 weeks · fixed price

    We audit a sample of your labelled studies, test the model on your hardest finding classes, map the PACS integration path and compliance requirements, and hand back a feasibility report with ROI maths — yours to keep. If you proceed, the Sprint cost is credited against the build.

  2. Build

    Compliance and audit architecture first, then the model and PACS integration. We design data residency, audit logs, and role-based access up front, and integrate with your PACS, RIS, and reporting templates — including the older systems.

  3. Production & continuous learning

    Clinical deployment with a radiologist in the loop, an ops dashboard, and change management for the radiology team. Radiologist corrections feed the model, so performance improves with each month of real use.

Frequently asked questions

It depends on the finding and its prevalence. High-frequency, morphologically consistent findings — chest nodules, retinal disease grades — can reach a usable baseline with a few thousand labelled studies. Rare or highly variable findings take more. The Discovery Sprint establishes the realistic baseline before you commit to a build.

In most cases yes. Banao integrates via HL7 DICOM and vendor APIs where they exist, and we have worked with locked PACS viewers through alternative feed paths. The week-one audit establishes exactly what is possible — we do not quote a build before we know the integration path.

It depends on the intended use and jurisdiction. Decision-support tools with a radiologist in the loop sit in a different regulatory class than autonomous diagnostic devices. We scope the regulatory path as part of the Discovery Sprint, and we will not recommend a build that ignores it.

The AI surfaces findings and flags discrepancies — the radiologist signs off every report. Their corrections are logged and feed back into the model's continuous learning pipeline. We design the workflow so the AI assists rather than interrupts, which is what drives adoption in practice.

Data residency stays in your jurisdiction by default. Inference can run on-premises or in your cloud tenancy. Audit logs, encryption at rest and in transit, and role-based access are defaults from the first sprint — HIPAA, DISHA, UAE PDPL, and Saudi NPHIES controls are not add-ons.

Put your hardest imaging backlog in front of our team

Bring your highest-volume modality and your toughest finding class. In 45 minutes we will tell you whether custom imaging AI is worth building, what the data and integration path looks like, and what the ROI maths say.

Book a 45-min scoping call