Viz.ai

Bottom line

Viz.ai is the market leader in AI-powered stroke and cardio coordination, deployed across 1,700+ US hospitals with 50+ FDA 510(k) clearances for automated detection of large vessel occlusion, intracranial hemorrhage, and pulmonary embolism. The platform excels at care coordination, not just detection: it automates alerting, transfer workflows, and multi-disciplinary team mobilization in time-sensitive neurological and cardiac emergencies.

Pricing is enterprise-only, structured as per-site subscriptions with no public rate cards. Expect six-figure annual contracts for comprehensive stroke centers and integrated delivery networks. The platform is purpose-built for high-volume stroke programs with established neurovascular teams, EHR infrastructure, and institutional commitment to workflow redesign.

Best fit: comprehensive stroke centers, Level 1 trauma centers with thrombectomy capability, IDNs coordinating spoke-and-hub transfers, and facilities with air ambulance partnerships. Skip if you are a community hospital without neurovascular specialists on-site, a solo radiology practice, or an organization unwilling to absorb significant implementation and training overhead.

Why we picked it

Viz.ai stands apart in the stroke AI category for depth of FDA validation and operational scale. While competitors like RapidAI and Brainomix offer detection algorithms, Viz.ai has built a full care-coordination layer on top of detection: automated alerts to neurology teams, transfer center workflows, and pre-arrival notifications to cath labs and OR suites. This addresses the core bottleneck in stroke care, which is not radiologist interpretation speed but rather the time from scan to treatment decision.

The platform holds clearances for LVO detection, ICH detection, ICH volume calculation, aortic dissection detection, and pulmonary embolism detection. This breadth allows a single vendor relationship to cover multiple high-acuity pathways rather than fragmenting AI tooling across specialties. For stroke coordinators managing hub-and-spoke networks, this consolidation reduces IT integration burden and training fragmentation.

Deployment scale matters in this category. At 1,700+ hospitals, Viz.ai has navigated the full range of EHR configurations, PACS vendors, and institutional workflows. Early adopters bore the cost of integration debugging; later adopters benefit from mature playbooks. The meta-analysis published in Translational Stroke Research 2025 pooled outcomes across multiple sites, demonstrating reproducibility rather than single-center proof-of-concept results.

The care-coordination workflow layer is the differentiator that justifies the enterprise price point. Automated LVO detection without downstream workflow automation still requires manual phone trees and pager cascades. Viz.ai embeds the entire treatment pathway into one platform, reducing door-to-groin puncture times by mobilizing interventional teams while the patient is still in the CT scanner.

What it does well

Viz.ai automates large vessel occlusion detection on CT angiography with sensitivity and specificity benchmarks that meet or exceed human readers in published validations. The system analyzes head CTA studies within 1 to 2 minutes of scan completion, flags suspected LVO cases, and pushes mobile notifications to neurology, interventional radiology, and stroke coordinator teams simultaneously. This parallel alerting collapses the serial workflow of radiologist read, then attending notification, then team mobilization.

The platform extends beyond stroke into cardio and pulmonary pathways. FDA-cleared modules detect aortic dissection on chest CTA and pulmonary embolism on CTPA. For hospitals managing multiple high-acuity imaging pathways, this allows centralized AI oversight rather than siloed vendor relationships. The ICH volume calculation tool, validated in the Journal of NeuroInterventional Surgery 2025, automates a previously manual and time-consuming task, providing volumetric measurements that guide surgical decision-making for hemorrhagic stroke.

Care coordination features include automated transfer center workflows, pre-arrival cath lab notifications, and real-time case tracking dashboards. The Life Flight integration pilot published in the Journal of Stroke and Cerebrovascular Diseases 2026 demonstrated earlier incorporation of air ambulance teams into transfer decisions, reducing ground-to-air handoff delays. These operational features address the gap between detection and treatment execution, which is where time losses accumulate in real-world stroke pathways.

The platform generates longitudinal quality metrics: door-to-detection time, detection-to-notification time, notification-to-groin puncture time, and transfer coordination intervals. These metrics feed directly into Joint Commission stroke certification reporting requirements and internal quality improvement cycles. For stroke program directors, this eliminates manual chart abstraction for performance dashboards.

Where it falls short

Pricing opacity is the most significant barrier to adoption. Viz.ai does not publish rate cards, tier structures, or ballpark ranges. Prospective buyers face a multi-month RFP and contracting process with no upfront cost anchoring. For community hospitals evaluating stroke AI on a defined capital budget, this creates planning friction. Enterprise per-site subscription models typically start in the low six figures annually for comprehensive stroke centers, with incremental costs for add-on modules like PE or aortic dissection detection.

The platform is narrowly scoped to vascular emergencies: stroke, aortic dissection, and pulmonary embolism. It does not address broader radiology AI use cases like incidental lung nodule detection, fracture detection, or general workflow prioritization. Hospitals seeking a unified AI radiology platform will need to layer additional vendors on top of Viz.ai, increasing integration complexity and vendor management overhead. Competitors like Aidoc offer broader pathology coverage within a single contract.

False positive rates, while not quantified in available public validations, remain a practical concern in any automated alerting system. Over-alerting desensitizes clinical teams and erodes trust in AI notifications. The Cureus 2024 ICH validation study noted the need for radiologist confirmation before clinical action, which preserves the human-in-the-loop requirement and limits time savings when radiologist availability is the true bottleneck. Training overhead for radiologists, ED physicians, and stroke coordinators is non-trivial: workflows must be redesigned around mobile alerts, and institutional protocols must define escalation paths for AI-flagged cases.

EHR integration depth varies by vendor. The platform integrates with PACS for image access but relies on HL7 feeds or APIs for patient demographic data and order context. Bi-directional EHR write-back for automated documentation is not a standard feature, requiring manual case entry for longitudinal tracking outside the Viz.ai platform. Epic-heavy systems may achieve tighter integration than community hospitals on smaller EHR vendors, creating a two-tier implementation experience.

Deployment realities

Implementation timelines range from three to six months for a single-site comprehensive stroke center, extending to 12 months for multi-site IDN rollouts. The core technical integration connects Viz.ai to the hospital PACS via DICOM routing rules, forwarding head CT and CTA studies to the Viz.ai cloud processing pipeline. IT teams must configure firewall rules, ensure PHI-appropriate data transmission agreements, and validate study routing logic to avoid missed cases or duplicate sends.

Training requires multi-disciplinary engagement. Radiologists learn to interpret AI-generated LVO annotations and integrate them into final reads. ED physicians adopt mobile app workflows for real-time case notifications. Stroke coordinators redesign transfer center protocols to incorporate automated alerts. Interventional radiology teams adjust cath lab pre-arrival workflows based on inbound notifications. This breadth of stakeholder training is more extensive than single-specialty AI tools and requires dedicated project management and change-management resources.

Ongoing IT support includes monitoring DICOM routing rules, troubleshooting mobile app authentication issues, and managing software updates pushed by Viz.ai. The platform operates as a cloud-based SaaS service, reducing on-premise server overhead but requiring stable internet connectivity and institutional comfort with cloud-based PHI processing. Hospitals with strict on-premise data residency policies face architectural friction.

Pricing realities

Viz.ai structures pricing as enterprise per-site subscriptions, with costs scaling by hospital volume, module breadth, and network size. Public pricing is not disclosed. Industry sources suggest comprehensive stroke center deployments with LVO, ICH, and care coordination modules range from 150,000 to 300,000 USD annually per site. Add-on modules for PE detection, aortic dissection detection, or ICH volume calculation incur incremental fees. Multi-site IDN contracts may achieve per-site discounts but require centralized procurement and uniform deployment timelines.

Hidden costs include implementation fees, which cover on-site IT integration, DICOM routing configuration, and initial training. These fees typically range from 20,000 to 50,000 USD per site. Annual support and maintenance fees are bundled into subscription costs but may escalate with feature updates or expanded user counts. Per-API-call pricing models are not standard for Viz.ai; the subscription covers unlimited case volume within the contracted scope.

ROI calculations hinge on door-to-treatment time reductions and downstream cost avoidance from improved functional outcomes. The meta-analysis in Translational Stroke Research 2025 reported workflow time reductions, but direct cost-per-QALY calculations are not published. For hospitals with existing thrombectomy programs, the value proposition is operational efficiency and quality metric improvement. For hospitals building stroke programs from scratch, the capital outlay must compete with other stroke certification investments like telemedicine infrastructure and neurology recruitment.

Compliance + integration depth

Viz.ai holds 50+ FDA 510(k) clearances, covering LVO detection, ICH detection, ICH volume calculation, aortic dissection detection, and pulmonary embolism detection. These clearances position the platform as a Class II medical device under FDA regulatory oversight, with ongoing post-market surveillance obligations. The clearances apply to specific imaging protocols and scanner types; off-protocol studies may fall outside validated performance thresholds. Radiologists must understand these boundaries to avoid over-reliance on AI outputs for edge cases.

HIPAA compliance is standard, with business associate agreements governing PHI transmission to the Viz.ai cloud infrastructure. SOC 2 Type II certification and HITRUST validation are not explicitly confirmed in available public disclosures, which may raise questions for health systems with strict third-party risk management frameworks. Prospective buyers should request attestations during contracting.

EHR integration relies on HL7 interfaces for patient demographic data and order context, with DICOM for image transfer. The platform does not natively write results back into EHR radiology modules or stroke flowsheets, requiring manual documentation workflows for longitudinal case tracking. Epic-based systems may achieve custom integrations via App Orchard partnerships, but this is institution-specific and not a standard deployment feature. PACS compatibility is broad, supporting major vendors like GE, Philips, Siemens, and Fujifilm, but smaller regional PACS vendors may require custom DICOM routing logic.

Vendor stability + roadmap

Viz.ai was founded in 2016 and has since secured significant venture capital funding across multiple rounds, though specific Series designations and dollar amounts are not uniformly disclosed in public filings. The company is privately held with headquarters in the United States. Leadership includes clinician-founders with neurology and radiology backgrounds, positioning the vendor as clinician-informed rather than pure-play technology development.

The product roadmap has expanded from stroke-only detection at launch to multi-pathology coverage including cardio and PE modules. Publicly stated direction includes broader vascular pathology detection and deeper EHR integration, though specific feature timelines are not published. The 50+ FDA clearances signal ongoing regulatory engagement and iterative product development rather than a static feature set.

Customer references are embedded in peer-reviewed publications, with named institutions including comprehensive stroke centers and academic medical centers participating in validation studies. The 1,700+ hospital deployment claim suggests a large installed base, though geographic concentration and facility type breakdowns are not publicly detailed. The vendor has not been acquired as of 2026, maintaining independent operational control and product direction.

How it compares

RapidAI competes directly in the stroke detection space with similar LVO and ICH modules. RapidAI emphasizes perfusion imaging analysis and automated ASPECTS scoring, appealing to stroke neurologists who prioritize penumbra quantification over pure vessel occlusion detection. RapidAI pricing is similarly enterprise-focused but with a reputation for more transparent tier structures. For hospitals prioritizing perfusion workflows, RapidAI may offer a better feature fit. For hospitals prioritizing care coordination and transfer workflows, Viz.ai holds the edge.

Brainomix e-Stroke offers ASPECTS scoring automation and LVO detection with a focus on European and UK markets, where NICE guidelines and NHS procurement frameworks favor cost-effectiveness validation. Brainomix pricing is more accessible for smaller stroke centers, and the platform integrates with UK-specific EHR systems like Lorenzo and Cerner Millennium UK editions. For US-based comprehensive stroke centers, Viz.ai's domestic deployment scale and FDA clearance breadth provide greater regulatory confidence.

Aidoc positions itself as a broader radiology AI platform covering stroke, PE, ICH, cervical spine fractures, and incidental findings across multiple body systems. For hospitals seeking a single vendor for comprehensive radiology AI, Aidoc reduces vendor fragmentation. However, Aidoc's care coordination features are less mature than Viz.ai's purpose-built stroke workflows. Hospitals with narrow stroke-only AI needs may find Aidoc's broader scope unnecessary and potentially more expensive per-pathology.

Viz.ai wins when care coordination and transfer workflows are the primary bottleneck, when FDA clearance breadth is a procurement requirement, and when the hospital has institutional capacity to absorb enterprise pricing and implementation overhead. Competitors win when perfusion imaging is central to stroke protocols (RapidAI), when budget constraints favor smaller vendors (Brainomix), or when broader radiology AI coverage is needed beyond vascular emergencies (Aidoc).

What clinicians say

Reddit and public clinician forums contain zero substantive discussions of Viz.ai as of the search date. This absence is notable given the platform's 1,700+ hospital deployment scale and decade-long market presence. The lack of organic clinician commentary may reflect the enterprise sales model: purchasing decisions are made by stroke program directors, CMIOs, and radiology chairs rather than frontline radiologists and ED physicians who populate online forums.

The absence of public clinician feedback creates an evidence gap for prospective buyers seeking peer validation outside vendor-curated case studies. Hospitals evaluating Viz.ai should request direct customer references and site visit opportunities to observe the platform in operational use. The meta-analysis in Translational Stroke Research 2025 pooled outcomes across multiple centers, providing institutional-level validation, but individual clinician satisfaction data remains unpublished in peer-reviewed or public forums.

This lack of community signal is not unique to Viz.ai; most enterprise radiology AI vendors see limited organic discussion relative to EHR platforms or clinical decision support tools. However, the absence means this review cannot incorporate frontline user sentiment, and prospective buyers should treat this section as a preliminary placeholder pending future community feedback accumulation.

What the literature says

The strongest evidence base comes from the meta-analysis published in Translational Stroke Research 2025, which pooled outcomes from multiple stroke centers using Viz.ai for automated LVO detection. The study reported improvements in workflow metrics including time from imaging to treatment decision, though absolute time reductions and confidence intervals are summarized at the systematic review level rather than raw trial data. The meta-analysis design aggregates heterogeneous center protocols, providing external validity at the cost of granular operational detail.

The Cureus 2024 retrospective study validated Viz.ai ICH detection accuracy, noting rapid flagging within 1 to 2 minutes of scan completion and improved diagnostic speed relative to unassisted radiologist interpretation. However, the study also noted that radiologist confirmation remained necessary before clinical action, preserving the human-in-the-loop workflow and limiting the degree of automation achieved in practice. The Journal of NeuroInterventional Surgery 2025 validation of the ICH volume calculation tool found accuracy comparable to manual segmentation methods, addressing a previously time-intensive task in hemorrhagic stroke management.

The scoping review in Medicina 2026 positioned Viz.ai alongside RapidAI, Brainomix, and Aidoc, noting Viz.ai's emphasis on care coordination workflows as a distinguishing feature. The review highlighted diagnostic accuracy parity across platforms but identified Viz.ai's hub-and-spoke transfer coordination features as unique in the competitive landscape. The Journal of Stroke and Cerebrovascular Diseases 2026 Life Flight pilot study demonstrated earlier air ambulance engagement in stroke transfers, though the study was a single-center pilot without control group comparison. Collectively, the literature supports Viz.ai's operational claims but highlights the need for larger randomized trials with patient outcome endpoints rather than workflow surrogates.

Who it's for

Viz.ai is purpose-built for comprehensive stroke centers with established neurovascular programs, interventional radiology teams, and institutional infrastructure for acute stroke care. The platform fits IDNs coordinating hub-and-spoke stroke networks, where transfer workflows and pre-arrival coordination are operational priorities. Level 1 trauma centers with thrombectomy capability, neurocritical care units, and 24/7 interventional radiology coverage will extract the most value from care coordination automation.

The platform also suits hospitals with air ambulance partnerships or Life Flight agreements, where early engagement of transfer teams reduces ground-to-air handoff delays. Stroke program directors seeking Joint Commission certification or seeking to improve publicly reported quality metrics will benefit from automated performance dashboards and longitudinal case tracking. CMIOs and radiology informatics leaders managing multiple high-acuity imaging pathways (stroke, PE, aortic dissection) will appreciate the consolidated vendor relationship and unified alerting infrastructure.

Skip Viz.ai if you are a community hospital without on-site neurovascular specialists, a solo radiology practice without stroke program infrastructure, or a facility where stroke volumes do not justify six-figure annual subscriptions. Budget-constrained organizations seeking stroke AI on limited capital should evaluate Brainomix or wait for market pricing evolution. Hospitals unwilling to invest in multi-disciplinary training and workflow redesign will not realize the platform's care coordination value, reducing it to an expensive LVO detection tool that does not justify the enterprise price premium over simpler alternatives.

The verdict

Viz.ai is the market-leading stroke and cardio coordination platform, validated by 50+ FDA clearances, 1,700+ hospital deployments, and a growing peer-reviewed evidence base. The platform excels at care coordination automation, addressing the operational bottleneck between imaging detection and treatment execution. For comprehensive stroke centers with institutional resources and high stroke volumes, Viz.ai delivers measurable workflow improvements and positions the organization at the leading edge of stroke AI adoption.

However, enterprise pricing opacity and narrow pathology scope create barriers for smaller hospitals and budget-conscious buyers. The absence of public clinician feedback limits peer validation, requiring prospective buyers to rely on vendor-curated references and published literature. The platform assumes significant IT integration capacity, multi-disciplinary training resources, and institutional tolerance for workflow redesign, which are not universally present across the stroke care landscape.

Recommendation: If you are a comprehensive stroke center or IDN stroke network with thrombectomy capability, neurovascular specialists, and budget flexibility, Viz.ai is the evidence-supported choice for care coordination automation. If you are a community hospital without on-site interventional capacity, evaluate Brainomix for lower-cost LVO detection or wait for market alternatives. If you need broader radiology AI coverage beyond vascular emergencies, layer Aidoc on top or choose Aidoc as a primary vendor. Prospective buyers should request site visits to operational Viz.ai installations, demand transparent pricing tiers during RFP, and pilot the platform with a subset of stroke cases before committing to enterprise-wide deployment. The evidence base supports adoption for the right institutional profile, but the investment is substantial and the operational lift is real.