Viz.ai

Bottom line

Viz.ai is a hospital-scale AI platform built for stroke, pulmonary embolism, and cardiac event coordination, deployed at more than 1,700 U.S. hospitals. It holds 50+ FDA 510(k) clearances and operates as an enterprise subscription service. Pricing is not publicly disclosed and is negotiated per hospital site.

This tool fits comprehensive stroke centers, integrated delivery networks with dedicated neuro and cardio programs, and academic medical centers that need care-coordination workflow automation beyond pure image detection. It does not fit solo practices, outpatient-only clinics, or small community hospitals without stroke certification.

The evidence base supports workflow time savings and diagnostic accuracy improvements for large vessel occlusion and intracranial hemorrhage detection. Community discussion is thin, and deployment requires substantial IT and clinical buy-in. For hospitals with stroke programs and enterprise budgets, Viz.ai is a defensible choice. For resource-constrained settings or outpatient-focused practices, look elsewhere.

Why we picked it

Viz.ai stands apart from pure detection algorithms because it includes a care-coordination layer. The platform alerts stroke teams, transfers images, and tracks time-to-treatment metrics within the same interface. A 2025 meta-analysis in Translational Stroke Research found that Viz.ai implementation improved stroke workflow metrics across multiple centers. This coordination function matters more in acute stroke care than detection alone, because time-to-treatment drives patient outcomes.

The FDA clearance footprint is the widest in this category. Viz.ai holds clearances for large vessel occlusion detection, intracranial hemorrhage volume calculation, pulmonary embolism triage, and aortic dissection flagging. Competitors typically hold one or two clearances. This breadth allows hospitals to consolidate AI triage under a single vendor rather than managing multiple point solutions.

The hospital count (1,700+ sites) signals real-world adoption at scale. Most AI radiology tools report pilot deployments or regional clusters. Viz.ai has achieved national footprint in the United States, suggesting that the platform survives procurement scrutiny and delivers measurable value post-implementation. For CMIOs evaluating vendor stability, this deployment scale reduces adoption risk.

The tool prioritizes speed. Studies cite alert times of one to two minutes from scan completion to radiologist notification. In stroke care, where treatment windows are measured in minutes, this latency reduction translates directly to clinical benefit. The combination of speed, coordination workflow, and broad FDA clearance made Viz.ai the strongest pick in the stroke and cardio coordination category.

What it does well

The large vessel occlusion detection module flags CT angiography scans with suspected occlusions and routes alerts to on-call interventionalists. A 2025 meta-analysis found that Viz.ai reduced door-to-groin puncture time and improved functional outcomes at discharge. The system integrates with PACS and sends mobile alerts to stroke team members, bypassing email or pager delays.

The intracranial hemorrhage volume calculation tool, validated in a 2025 study in the Journal of NeuroInterventional Surgery, provides automated hematoma volume estimates within seconds. Manual volume calculation using the ABC/2 method takes minutes and introduces inter-rater variability. Automated volume estimation supports triage decisions for neurosurgical consultation and helps predict outcomes based on hematoma size.

The pulmonary embolism triage module flags CT pulmonary angiography scans with central or segmental emboli and calculates right ventricle to left ventricle ratio, a marker of hemodynamic instability. This allows emergency physicians to identify high-risk PE cases that may benefit from thrombolysis or mechanical thrombectomy, rather than anticoagulation alone.

The platform maintains a timestamped audit trail of alerts, acknowledgments, and case dispositions. This workflow tracking supports quality improvement initiatives and Joint Commission stroke certification documentation. Hospitals can measure door-to-notification time, notification-to-acknowledgment time, and total time-to-treatment across all stroke activations, identifying bottlenecks in the care pathway.

Where it falls short

Pricing opacity is the most significant barrier for smaller hospitals. Viz.ai does not publish per-site subscription costs, and contract terms are negotiated individually. This enterprise sales model excludes community hospitals, critical access hospitals, and solo radiology groups that lack procurement budgets or negotiating leverage. Competitors like Aidoc and RapidAI also use enterprise pricing, so this is a category-wide issue, but it remains a limitation.

Community discussion is sparse. Only one Reddit post in the radiology subreddit mentions Viz.ai, asking neutrally whether anyone has experience with the platform. This silence may indicate that users discuss the tool in closed professional forums rather than public spaces, or that adoption remains concentrated in large academic centers with limited representation in online physician communities. Either way, independent user reviews are hard to find.

The evidence base, while growing, is still early. Most published studies date from 2024 to 2026. A scoping review in Medicina (2026) positions Viz.ai alongside Brainomix, Aidoc, and RapidAI as leading platforms, but notes that head-to-head comparative effectiveness trials are lacking. Hospitals evaluating these tools cannot rely on direct comparison data and must instead extrapolate from single-arm studies and workflow audits.

The platform is specialty-specific. Viz.ai focuses on stroke, pulmonary embolism, and cardiac conditions. Hospitals seeking a general-purpose AI radiology tool for lung nodules, fractures, or incidental findings will need additional vendors. This increases vendor management overhead and may fragment workflow if different AI tools use different alert mechanisms.

Deployment realities

EHR integration depth is not documented in available sources, a gap that CMIOs will need to address during vendor evaluation. The platform integrates with PACS systems to pull imaging studies, but whether it writes structured findings back into Epic, Cerner, or Meditech flowsheets is unclear. Hospitals should ask whether the integration is read-only or bidirectional and whether custom HL7 interfaces are required.

Implementation requires coordination among radiology, neurology, interventional radiology, emergency medicine, and IT teams. A pilot study in the Journal of Stroke and Cerebrovascular Diseases (2026) described incorporating Life Flight air transport into the Viz.ai alert pathway, suggesting that deployment extends beyond hospital walls to include transfer protocols and inter-facility coordination. This complexity means that implementation timelines likely span months, not weeks.

Training overhead depends on role. Radiologists and stroke coordinators interact with the platform daily and need structured onboarding. Emergency physicians and interventionalists receive mobile alerts but may only engage with the full interface during stroke activations. Hospitals must plan for role-specific training modules and ensure that on-call coverage includes team members who are credentialed on the system.

Pricing realities

Viz.ai uses an enterprise subscription model with per-site pricing negotiated individually. No public pricing benchmarks are available. Hospitals should anticipate annual contracts with costs that scale based on imaging volume, number of licensed users, and included clinical modules (stroke, PE, cardiac). Industry norms for AI radiology subscriptions at comprehensive stroke centers range from low five figures to mid-six figures annually, depending on site volume.

Hidden costs include implementation fees, integration engineering (especially if custom HL7 interfaces are required), training time for clinical and IT staff, and ongoing support contracts. Hospitals should clarify whether software updates and new FDA-cleared modules are included in the base subscription or priced separately. If the vendor charges per-study fees on top of the subscription, total cost of ownership will be higher than the initial quote.

Return on investment hinges on measurable workflow improvements. The 2025 meta-analysis in Translational Stroke Research found reductions in door-to-treatment time and improvements in functional outcomes at discharge. If these time savings translate to fewer complications, shorter hospital stays, or better reimbursement under value-based contracts, the tool may pay for itself. Hospitals should model ROI using their own stroke volume and current workflow metrics rather than relying on vendor-provided case studies.

Compliance + integration depth

Viz.ai holds more than 50 FDA 510(k) clearances, covering large vessel occlusion detection, intracranial hemorrhage quantification, pulmonary embolism triage, and aortic dissection flagging. These clearances position the platform as a Class II medical device, meaning it has been reviewed for safety and effectiveness by the FDA. Hospitals can cite these clearances when responding to medical staff credentialing questions or Joint Commission surveys.

The platform is HIPAA compliant, a baseline requirement for any health IT tool handling protected health information. Available sources do not mention SOC 2 Type II or HITRUST certification, which some health systems require for third-party vendors. CMIOs should verify compliance certifications during procurement and confirm that Viz.ai signs business associate agreements that meet institutional legal standards.

Specific EHR integration depth is not documented in public sources. The tool integrates with PACS to retrieve imaging studies and send alerts, but whether it writes structured data back into Epic Beacon, Cerner PowerChart, or Meditech Expanse is unclear. Hospitals should ask whether the integration supports bidirectional data exchange, whether custom interface development is required, and what ongoing maintenance the integration demands.

Vendor stability + roadmap

Viz.ai was founded in 2016 and has achieved deployment at more than 1,700 U.S. hospitals, suggesting a mature commercial operation. The company headquarters is in the United States. Specific funding rounds, leadership team details, and acquisition history are not documented in available sources, a gap that hospitals should address during due diligence by requesting customer references and reviewing vendor financial stability.

The FDA clearance pipeline suggests an expanding product roadmap. The company has moved from stroke-only detection to pulmonary embolism, cardiac conditions, and aortic pathology. This trajectory indicates that Viz.ai is investing in multi-specialty AI modules rather than remaining a single-indication vendor. Hospitals that adopt the platform for stroke may benefit from future modules without switching vendors.

Customer references are implied by the 1,700-hospital footprint but are not named in public sources. Hospitals evaluating Viz.ai should request a list of peer institutions using the platform, ideally including academic medical centers and community hospitals of similar size and stroke volume. Site visits or peer calls can surface implementation challenges and workflow best practices that vendor sales materials omit.

How it compares

RapidAI competes directly in the stroke detection and workflow coordination space. A scoping review in Medicina (2026) evaluated RapidAI alongside Viz.ai and found both platforms effective for large vessel occlusion detection. RapidAI also offers perfusion imaging analysis and aneurysm detection modules. Hospitals with advanced stroke imaging protocols that include CT perfusion may prefer RapidAI. Viz.ai wins on FDA clearance breadth and coordination workflow depth.

Brainomix e-Stroke offers automated large vessel occlusion detection and perfusion analysis with a focus on international markets, particularly Europe and Asia. The same scoping review found diagnostic accuracy comparable to Viz.ai. Brainomix may be a better fit for health systems outside the United States or for hospitals that prioritize perfusion analysis over care coordination alerts. Viz.ai's U.S. market dominance and care-team notification features give it an edge in domestic stroke centers.

Aidoc provides a broader AI triage platform covering stroke, PE, intracranial hemorrhage, and incidental findings like pulmonary nodules and fractures. Hospitals seeking a single vendor for general radiology AI may prefer Aidoc over Viz.ai's specialty focus. Viz.ai wins when stroke and cardio coordination are the primary use case and when the hospital values deep workflow integration over breadth of detection algorithms.

For hospitals prioritizing cost transparency, none of these vendors publish pricing, so comparison shopping requires parallel procurement negotiations. For hospitals prioritizing evidence depth, the 2025 meta-analysis in Translational Stroke Research specifically evaluated Viz.ai and found workflow improvements, giving it a marginal edge in published validation. Hospitals should request head-to-head performance data during vendor evaluation, though such data are scarce.

What clinicians say

Community discussion is limited. Clinicians on r/Radiology posted a single neutral inquiry asking whether anyone had experience with Viz.ai or RapidAI for stroke detection assistance. No replies were documented in available sources, suggesting that user feedback is either concentrated in closed professional forums, institutional Slack channels, or vendor-mediated user groups rather than public Reddit threads.

This silence is notable. Other AI radiology tools generate polarized Reddit discussions about alert fatigue, false positives, and workflow friction. The absence of such commentary for Viz.ai may indicate low community penetration, high user satisfaction (less motivation to vent), or that adopters are predominantly academic centers where clinicians discuss tools internally rather than online. Hospitals should request peer references from the vendor and conduct site visits to surface candid user feedback.

The lack of independent user reviews online is a limitation for smaller hospitals that cannot access peer networks easily. Relying solely on vendor-provided case studies and curated testimonials increases adoption risk. Hospitals should plan for a pilot deployment with defined success metrics and exit criteria rather than committing to multi-year enterprise contracts based on vendor claims alone.

What the literature says

A 2025 meta-analysis in Translational Stroke Research systematically reviewed Viz.ai's impact on stroke workflow metrics and patient outcomes. The analysis found that Viz.ai implementation reduced door-to-groin puncture time and improved functional outcomes at discharge. These findings support the platform's value proposition that faster triage and care-team coordination translate to measurable clinical benefit in acute stroke care.

A retrospective study in Cureus (2024) validated the Viz.ai intracranial hemorrhage detection module and found that the tool alerted radiologists to hemorrhage presence within one to two minutes of scan completion. The study concluded that faster diagnosis enabled earlier treatment decisions. A separate real-world evaluation in the Journal of NeuroInterventional Surgery (2025) assessed the accuracy of Viz.ai's automated hemorrhage volume calculation and found it comparable to manual methods, with the advantage of eliminating inter-rater variability.

A pilot study in the Journal of Stroke and Cerebrovascular Diseases (2026) described integrating Viz.ai into a telestroke network that included air transport coordination with Life Flight. The study found that early incorporation of transfer logistics into the AI alert pathway reduced time-to-transfer for patients requiring thrombectomy at distant comprehensive stroke centers. This suggests that Viz.ai's coordination features extend beyond single-hospital workflows to hub-and-spoke telestroke networks.

A scoping review in Medicina (2026) compared Viz.ai to Brainomix, Aidoc, and RapidAI and concluded that all four platforms demonstrated diagnostic accuracy for stroke detection, but that head-to-head comparative effectiveness trials were lacking. The review noted that cost-effectiveness data were scarce and that most studies were funded or co-authored by the vendors themselves, introducing potential bias. Hospitals should interpret published studies cautiously and prioritize peer references and pilot data over vendor-sponsored research.

Who it's for

Comprehensive stroke centers with Joint Commission certification and dedicated interventional neuroradiology programs are the ideal fit. These facilities have the case volume, subspecialty teams, and IT infrastructure to justify enterprise deployment. Academic medical centers and Level 1 trauma centers also fit this profile. Viz.ai's care-coordination features align with the workflow complexity of these environments.

Integrated delivery networks managing hub-and-spoke telestroke programs can benefit from Viz.ai's alert routing and transfer coordination features. The pilot study in the Journal of Stroke and Cerebrovascular Diseases (2026) demonstrated value in this use case. IDNs that include both primary stroke centers and comprehensive stroke centers can use Viz.ai to standardize triage and streamline inter-facility transfers.

Small community hospitals without stroke certification, solo radiology practices, and outpatient imaging centers should skip Viz.ai. The enterprise pricing model and workflow complexity exceed the needs and budgets of these settings. Critical access hospitals and rural facilities with limited neuro subspecialty access may find the platform overkill unless they participate in a regional telestroke network that has already adopted Viz.ai at the hub site.

The verdict

Viz.ai is a defensible choice for comprehensive stroke centers and integrated delivery networks with dedicated neuro and cardio programs. The evidence supports workflow time savings, diagnostic accuracy for large vessel occlusion and intracranial hemorrhage, and functional outcome improvements. The FDA clearance footprint is the widest in this category, and the 1,700-hospital deployment scale signals vendor stability.

Proceed with caution on three fronts. First, pricing transparency is absent, requiring negotiation and internal budget approval without public benchmarks. Second, community discussion is thin, forcing reliance on vendor-provided references and pilot deployments rather than independent user reviews. Third, EHR integration depth is not documented in public sources, requiring due diligence during procurement to confirm bidirectional data exchange and avoid custom interface costs.

If your institution is a comprehensive stroke center with enterprise procurement budget and you prioritize care-coordination workflow automation over general-purpose radiology AI, Viz.ai is the leading option. If budget is constrained, stroke volume is low, or you need a multi-specialty AI tool covering lung, bone, and incidental findings, evaluate Aidoc or broader radiology AI platforms instead. If you operate outside the United States or prioritize perfusion imaging analysis, compare RapidAI and Brainomix head-to-head with Viz.ai before deciding. Plan for a pilot deployment with exit criteria tied to measurable workflow improvements rather than committing to multi-year contracts based on vendor claims alone.