Reduce time for stroke detection by 44%

Target Audience

Emergency department physicians, healthcare researchers, hospital network administrators

Challenge

Stroke treatment outcomes depend critically on rapid response. Delays in mechanical thrombectomy often stem from slow diagnosis and fragmented communications among care teams. Traditional workflows involve complex pathways and communication barriers that delay critical treatment.

Solution Approach

Viz.ai uses AI algorithms to automatically analyze CT angiogram scans and detect potential large vessel occlusions within minutes. When a suspected LVO is detected, the platform immediately alerts the entire on-call stroke care team via mobile notifications with HIPAA-compliant secure messaging and real-time image sharing.

Value Add

A systematic review of 12 studies involving 15,595 patients demonstrated significant reduction in CT-to-treatment time. The system reduced door-to-groin puncture time by 11 minutes, and achieved 44% reduction in time from arrival to diagnosis. Now deployed across 1,500+ hospitals covering 230+ million lives in the U.S.

How to Apply This

Creating AI solutions for other healthcare related challenges mostly require access to high-quality patient data. Therefore, and use case must be founded in rigorous data protection. Further on, this limits your possibilities to run data processing and AI training on any third-party cloud provider. As a start, you can assess the available data together with their usage purposes to identify potential gaps. Then, a first phase of a project should focus on closing any gaps in the data and ensuring the data quality needed to create a good AI solution.

Have questions on how to run AI projects in highly regulated domains while keeping strict GDPR compliance? Reach out to me and book a free 30-minute feasibility call.