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MRI Reading & AI Recommendations
Possible findings flagged for radiologist review.
The challenge
Radiologists face growing MRI volumes. Findings, prior studies and reports must be brought together under time pressure, and privacy rules limit which tools can touch patient data.
What we built
A medical imaging solution designed to read MRI scans and associated radiology reports, flag possible findings, highlight regions of interest and summarise key observations. Patient identifiers are removed before AI review through an offline privacy workflow that supports Arabic and UAE specific identifiers, and audit trails record processing and recommendations.
The AI layer
- Recommendations for radiologist consideration, with confidence levels and supporting reasoning.
- Outputs support radiologist decision making. Human review and clinical validation are required before clinical use.
How it works
The workflow, step by step.
- 01Scan and report received
- 02Identifiers removed offline
- 03AI reviews images and report
- 04Findings and recommendations
- 05Radiologist decides
Architecture
How the pieces connect.
Inputs
- MRI series
- Radiology reports
- Prior studies
The system
- Scan & report reading
- Findings & regions
- Recommendations
- De-identification
- Audit trail
- Radiologist review
AI
- Recommendations for radiologist consideration
- Outputs support radiologist decision making
People
- Radiologist
- Department lead
Outputs
- Flagged findings
- Regions of interest
- Recommendations with confidence
- Audit trail
Functional architecture. Integrations and hosting are tailored to each client's environment.
Modules
Everything in one system.
01Scan & report reading
MRI scans read with their radiology reports.
02Findings & regions
Possible findings flagged and regions highlighted.
03Recommendations
Follow-up, referral and prior comparison suggestions.
04De-identification
Offline removal of identifiers, including Arabic and UAE formats.
05Audit trail
Every processing step and recommendation recorded.
06Radiologist review
Outputs support, never replace, the radiologist.
Interface preview
What your team would see.
Illustrative interface · sample data
Who uses it
Built around real roles.
Radiologist
Reviews every finding and makes the call.
Department lead
Oversees the queue, audit and quality.
A brain MRI arrives with its report. Identifiers, including an Arabic name and an Emirates ID number, are removed offline. The AI highlights a region of interest and suggests comparison with a prior study, with a stated confidence level, and the radiologist makes the call.
Build your version
Start here, extend further.
Capabilities we can build on this foundation for your version of the system.
- PACS and RIS integration for worklist-driven review
- Structured reporting templates
- Longitudinal comparison across prior studies
- Turnaround and quality dashboards
- A pathway to clinical validation studies and regulatory approval
Questions
What clients ask us.
How is patient data protected?
Identifiers are removed before AI review through an offline workflow that supports Arabic and UAE specific identifiers, and every step is recorded in an audit trail.
Can it be used clinically today?
Outputs support radiologist decision making. Human review and clinical validation are required before clinical use.
Can you build a version of this for our business?
Yes. Every system starts with a discovery conversation about your workflow, data and goals, and we adapt the design to fit. Most engagements produce a working, testable system within a few weeks to a couple of months, depending on scope.
Can it integrate with the systems we already use?
Integration with existing tools such as ERPs, accounting packages, databases and document stores is scoped during discovery, so the system fits around how you already work.
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