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What is Viz.ai?

Viz.ai is an AI platform designed to improve stroke diagnosis and patient care coordination in hospital settings. The tool uses computer vision to analyse medical imaging, particularly CT scans, to help identify signs of stroke quickly. By flagging potential stroke cases automatically, it aims to reduce time to diagnosis and treatment, which is critical since stroke outcomes depend heavily on rapid intervention. The platform focuses on care coordination alongside detection, helping hospitals simplify communication between emergency departments, radiology teams, and stroke specialists. This is intended to reduce delays in the care pathway. Viz.ai is available free to healthcare institutions, making it accessible to hospitals regardless of budget constraints. The tool integrates with existing hospital systems rather than requiring separate workflows.

Key Features

Automated stroke detection

AI analysis of CT imaging to identify potential stroke cases

Care coordination tools

Integrated communication features to connect relevant clinical teams

Time tracking

Monitors time from imaging to specialist review to identify bottlenecks

Hospital system integration

Works within existing electronic health record systems

Clinical alerts

Notifies appropriate staff when potential stroke cases are identified

Imaging analysis

Computer vision assessment of brain scans for ischaemic stroke indicators

Pros & Cons

Advantages

  • Free to use, removing financial barriers for hospitals to adopt stroke detection tools
  • Addresses a genuine clinical need; faster stroke detection directly improves patient outcomes
  • Integrates with existing hospital infrastructure rather than requiring separate systems
  • Supports the entire care pathway, not just detection

Limitations

  • Effectiveness depends on image quality and hospital integration; results may vary across different institutions
  • Requires staff training and buy-in for adoption to be successful
  • As an AI tool, it requires oversight; it should be used to support clinical judgement, not replace it

Use Cases

Emergency departments seeking to reduce time between patient arrival and stroke specialist assessment

Hospitals wanting to improve stroke protocol compliance and consistency

Healthcare systems aiming to identify and reduce delays in their stroke care pathway

Rural or smaller hospitals with limited immediate access to specialist radiologists