MedTech AI Diagnostic Imaging Platform — UK Radiology Group

MHRA Class IIa

AI diagnostic triage that informs but does not replace radiologist decision.

Clinical evaluation

systematic literature review of AI diagnostic performance evidence (AUC ≥ 0.92 required for pneumothorax detection).

Intended purpose limitation

triage support only — AI highlights probable urgent findings to radiologist, does not output a diagnosis.

Technical file

classification rationale, clinical evaluation report, post-market surveillance plan.

AI Model Architecture

Chest X-ray classification model: EfficientNet-B4 pretrained on ImageNet, fine-tuned on CheXpert dataset (224,316 chest radiographs with radiologist labels).

UK training data

MIMIC-CXR (MIT + Beth Israel) supplemented with 12,000 de-identified NHS chest X-rays under HRA research ethics approval.

Output

5 urgent findings probability scores (0–1) + triage tier (urgent/routine/normal).

Inference

• AWS SageMaker real-time endpoint (p95 latency &lt
• 200ms for 512×512 DICOM image).

DICOMweb PACS Integration

• DICOMweb WADO-RS (Web Access to DICOM Objects — Retrieve Service): retrieve DICOM images from any standards-compliant PACS.
• QIDO-RS (Query Instance): query for new studies in worklist.
• STOW-

RS

store AI-annotated DICOM with triage annotations.

Integration tested against

• Sectra IDS7, Philips Vue PACS, Fujifilm Synapse — all three support DICOMweb.
• DICOM de-identification: patient identifiers stripped before AI inference (AI receives anonymised DICOM — re-identified for worklist display after inference).

Triage dashboard

worklist sorted by urgent finding probability (highest first).

Radiologist overlay

• AI confidence heatmap overlaid on DICOM viewer (indicating which region triggered the triage flag).
• One-click override: radiologist can downgrade AI urgent flag to routine with free-text reason (logged for post-market surveillance).

Audit log

every AI triage decision, every radiologist override, and every report outcome — feeds post-market clinical follow-up (PMCF) study.