Description
Viz.ai is a clinical AI platform combining medical imaging analysis with care coordination, built to detect time-critical conditions and immediately alert the right specialist rather than just flagging a finding in a queue. It built its initial reputation on large vessel occlusion (LVO) stroke detection and has since expanded to a portfolio of 50+ FDA-cleared algorithms spanning neurology, cardiovascular, vascular, trauma, radiology, and pulmonary care, deployed across more than 1,700 hospitals. The platform’s core workflow analyzes CT angiography and other imaging, and when it detects a potential emergency like a stroke, it sends a direct alert to the on-call specialist’s phone rather than relying on the radiologist’s standard reporting queue. It suits hospital systems that want a single broad clinical AI platform spanning multiple specialties and time-sensitive conditions, with coordination built in rather than added separately.
Key Features
- 50+ FDA-cleared algorithms — spans neuro, cardiovascular, vascular, trauma, radiology, and pulmonary conditions.
- LVO stroke detection — the platform’s original and most established use case, analyzing CT angiography for large vessel occlusion.
- Direct specialist alerting — sends real-time notifications to the on-call specialist’s phone when a critical finding is detected.
- Care coordination workflow — built to move a patient from detection to treatment faster, not just flag a finding.
- Multi-specialty coverage — one platform spanning several time-critical clinical areas rather than a single-condition tool.
- Viz.ai One — a unified platform experience tying detection, alerting, and coordination together.
How It Works
When a patient gets a CT angiography scan or other supported imaging study, Viz.ai’s algorithms analyze the images for signs of a time-critical condition, such as a large vessel occlusion stroke, a pulmonary embolism, or a traumatic injury. If a positive finding is detected, the platform doesn’t stop at flagging the study in a radiologist’s worklist; it sends a direct alert to the relevant on-call specialist’s phone, aiming to compress the time between detection and treatment decision. Because the algorithm portfolio spans multiple specialties rather than one condition, a hospital can deploy Viz.ai across neurology, cardiology, vascular surgery, and trauma programs under one platform rather than running separate point solutions for each. The care coordination layer connects the clinical teams involved in a given emergency, whether that’s a stroke team, a cath lab, or a trauma surgeon, so the alert reaches whoever needs to act, not just the reading radiologist.
Technical Architecture & Overview
- Core Engine: Proprietary FDA-cleared algorithms per condition; specific underlying model architecture is not published on Viz.ai’s official site.
- Deployment: Cloud-based platform (Viz.ai One) integrating with hospital imaging and communication systems.
- API Surface: Integrates with hospital PACS and care team communication systems as part of enterprise deployment.
- Known Limits: No public pricing or self-serve access; which conditions a hospital can detect and coordinate around depends on which of the 50+ cleared algorithms it licenses.
Pros & Cons
| Pros | Cons |
|---|---|
| Broadest FDA-cleared algorithm portfolio in this category, spanning six clinical areas | No published pricing; every deployment requires enterprise sales and IT integration |
| Direct specialist alerting compresses time from detection to treatment decision, not just flagging a finding | Full value depends on licensing multiple algorithm modules, which adds cost and complexity to scope |
| One platform spans multiple specialties, reducing the need for separate point solutions per condition | Care coordination workflow requires buy-in from multiple clinical teams to be effective, not just IT |
| Deployed across 1,700+ hospitals with a long track record starting from LVO stroke detection | Best suited to hospitals wanting breadth across specialties rather than the deepest single-specialty tool |
Our Take
Viz.ai’s real edge is coordination, not just detection — sending a direct alert to the on-call specialist’s phone the moment a critical finding shows up is a genuinely different workflow than a tool that just flags a study in a radiologist’s queue. Combined with 50+ cleared algorithms spanning six clinical areas, it’s built for hospitals that want breadth across time-critical conditions under one platform.
Not ideal for: a hospital looking for the single deepest tool in one narrow specialty — Viz.ai’s strength is breadth and coordination across conditions, so a system wanting maximum depth in, say, abdominal CT triage alone may find Aidoc’s foundation-model approach more targeted to that specific need.
Pricing
Viz.ai does not publish pricing on its official site. As with other enterprise clinical AI platforms, cost is set through direct sales engagement and depends on which of the 50+ FDA-cleared algorithm modules a hospital licenses, deployment scope, and integration requirements. There is no self-serve trial or public rate card; requesting a demo through Viz.ai’s site is the starting point for a quote.
Last verified: August 18, 2026
Platform Availability
Web | Mobile alerting (Viz.ai One)
Best For
Hospital stroke and neuro programs | Cardiovascular teams | Trauma centers | Multi-specialty health systems
Frequently Asked Questions
Does Viz.ai publish pricing?
No. Viz.ai is sold through direct enterprise sales, with cost depending on which algorithm modules a hospital licenses; there’s no published rate card.
What was Viz.ai’s original use case?
Large vessel occlusion (LVO) stroke detection from CT angiography scans, which remains one of its most established and cited capabilities.
How many FDA-cleared algorithms does Viz.ai have?
More than 50, spanning neurology, cardiovascular, vascular, trauma, radiology, and pulmonary conditions.
How does Viz.ai alert clinicians?
When a critical finding is detected, Viz.ai sends a direct notification to the relevant on-call specialist’s phone, rather than only flagging the study in a standard reading queue.
How does Viz.ai differ from Aidoc?
Viz.ai emphasizes breadth across 50+ algorithms and specialties plus built-in specialist alerting and care coordination, while Aidoc emphasizes the most FDA clearances in the category and a foundation-model approach to multi-condition triage from a single scan.
