Key Result
95% diagnostic accuracy
A major hospital group was struggling with significant radiologist bottlenecks that were impacting patient care across multiple facilities. The average report turnaround time had stretched to 72 hours, creating a growing backlog of patients waiting for critical diagnostic results.
Missed findings were increasing malpractice risk, and the growing patient backlog was directly affecting care quality. The hospital needed a solution that could assist radiologists without replacing their clinical judgment.
With increasing patient volumes and a shortage of specialist radiologists, the hospital group needed an AI-powered system to triage and prioritise cases, flagging potential anomalies for faster human review.
AINinza developed a custom computer vision model trained on 500,000+ annotated medical images spanning multiple imaging modalities. The model was designed to perform real-time analysis of incoming scans, identifying potential anomalies and flagging them for radiologist review.
The system was integrated directly with the hospital's existing PACS (Picture Archiving and Communication System), ensuring seamless adoption without disrupting established clinical workflows. The AI automatically prioritises urgent cases, allowing radiologists to focus their expertise where it matters most.
95%
Diagnostic Accuracy
40%
Faster Diagnosis
30%
Cost Per Scan Reduced
4 weeks
6 weeks
4 weeks
2 weeks