RadQA Book a demo

Radiology · Pilot

AI quality assurance for radiology.

Catch laterality conflicts, uncommunicated critical findings, protocol deviations, and image-quality defects before they leave the department — with evidence marked on the report and a human approving every write-back.

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Radiology and imaging departments, QA leads, radiographers, and radiologists who want systematic quality assurance without adding manual review load.

The problem

Radiology QA is mostly retrospective sampling done by hand. Discrepancies, template leftovers, missed critical-result communication, and acquisition errors slip through because nobody has time to check every study.

How it works

  1. 1

    Connect your systems

    Point RadQA at PACS, RIS, or FHIR, or start with the built-in demo dataset.

  2. 2

    Run QA

    Studies and reports are checked automatically as they arrive or on demand.

  3. 3

    Review the worklist

    Open a case to see findings with evidence marked directly on the report and images.

  4. 4

    Act with oversight

    Ask the agent to investigate; addenda and write-backs need explicit approval.

See it working

Every study checked in one pass on a demo dataset: report QA catching an uncommunicated critical finding, protocol and image QA, the agent drafting an addendum that waits for approval, analytics and connectors.

Inside RadQA

Report QA — a critical finding of bilateral pulmonary emboli is flagged because nothing in the report records that it was communicated, alongside two nodules described without a follow-up recommendation. Every finding quotes the sentence it came from.
Protocol QA — the template for a CT pulmonary angiogram expects IV contrast and specific series. RadQA flags the missing contrast, the acquisitions that never arrived, and a slice thickness above the diagnostic limit.
Images — DICOM is decoded in the app, with stack scroll, window and level, zoom and pan, and image-QA findings overlaid as regions on the pixels. Reading happens in dark mode, where the app follows your system theme.
The agent knows which case is open. Ask it to review the findings and it explains the likely root cause, ranks what matters clinically, and proposes next steps for you to approve or decline.
Analytics — volume, mean QA score, turnaround, SLA breaches, and the critical-communication gap across a chosen range, filterable by modality and by connector.
Connectors — studies and reports come in from FHIR servers, DICOMweb PACS, HL7 v2 RIS feeds over MLLP, a local folder, or the built-in demo dataset. Write-back goes out the same way.

What it does

Questions

Is RadQA a diagnostic tool?

No. It supports departmental quality assurance by flagging likely issues in reports, protocols, and images for a human to review. It does not diagnose or report studies.

Which systems can it connect to?

DICOMweb PACS (QIDO-RS / WADO-RS), HL7 v2 RIS feeds over MLLP, FHIR R4 servers, and local DICOM folders. Connectors are plugins, so new integrations can be added without changing the core.

Where does patient data go?

RadQA runs as a desktop workstation with local SQLite storage. Model-backed checks send only the text or image needed for that check to the configured model provider, using a key stored in the OS keychain.

Can I try it?

RadQA is in pilot. Register your interest and we will get in touch about running it against your department's systems.

Try RadQA in your department

RadQA is in pilot. Book a demo and we'll show it on synthetic data, then talk about running it on your own systems, under your governance.

Book a demo

Or email hi@zapxlabs.com.

RadQA is a quality-assurance assistant for imaging departments. It is not a diagnostic device and does not replace radiologist reporting or clinical judgement. Validate against your own systems and local governance before production use.