Viz.ai Review 2026: Features, Pricing & Comparison

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Overview

Viz.ai is a pioneering artificial intelligence platform revolutionizing emergency care for time-critical conditions through automated detection and intelligent care coordination. Founded in 2016 by Dr. Chris Mansi and Dr. David Golan, Viz.ai has deployed its FDA-cleared stroke detection algorithms across over 1,400 hospitals and healthcare systems, analyzing millions of CT scans to identify large vessel occlusions (LVO) - the most severe strokes requiring immediate intervention. The platform automatically alerts stroke teams within minutes of imaging completion, dramatically reducing time-to-treatment and improving patient outcomes in conditions where every minute matters.

What sets Viz.ai apart is its comprehensive approach to emergency care coordination, combining AI detection with intelligent communication workflows. When the AI identifies a suspected LVO stroke, it simultaneously alerts the entire stroke team - neurologists, interventionalists, emergency physicians - through a mobile app with complete imaging, patient information, and one-touch communication capabilities. This eliminates the traditional delays of phone calls, paging systems, and manual image review that waste precious minutes during stroke emergencies. The platform has expanded beyond stroke to include pulmonary embolism detection, aortic dissection identification, and other time-critical conditions where rapid diagnosis and treatment coordination save lives.

Viz.ai serves emergency departments, stroke centers, neurology teams, interventional radiologists, and hospital administrators seeking to optimize time-critical care pathways. Clinical studies demonstrate Viz.ai reduces door-to-treatment time by an average of 52 minutes for stroke patients, with corresponding improvements in patient outcomes and mortality. The platform has received multiple FDA clearances including breakthrough device designation, validating its clinical effectiveness. With continuous algorithm improvements, expanding clinical applications, and proven ability to accelerate care for life-threatening emergencies, Viz.ai represents the cutting edge of AI-powered emergency medicine transforming how hospitals respond to time-critical conditions.

Key Features

LVO Stroke Detection

FDA-cleared AI automatically analyzes CT angiography scans for large vessel occlusion strokes. Identifies blockages in major brain arteries requiring immediate thrombectomy with over 90% sensitivity.

Automated Care Team Alerts

Instantly notifies entire stroke team when LVO detected - neurologists, interventionalists, emergency physicians. Simultaneous alerts via mobile app eliminate communication delays.

Mobile App Coordination

Comprehensive care coordination app displays imaging, patient data, team communication, and workflow tracking. One-touch calling, messaging, and case management streamline emergency response.

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Pulmonary Embolism Detection

AI analyzes chest CT scans for pulmonary embolism - blood clots in lung arteries. Alerts appropriate specialists for timely anticoagulation or intervention in high-risk cases.

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Aortic Dissection Detection

Identifies suspected aortic dissections on CT imaging - life-threatening tears in the aorta requiring emergency surgery. Accelerates diagnosis and surgical team activation.

Treatment Planning Support

Provides imaging analysis, vessel measurements, and procedural planning information to interventionalists. Helps teams prepare for thrombectomy while patient is en route to cath lab.

FDA Cleared Algorithms

Multiple FDA clearances including breakthrough device designation for stroke detection. Clinically validated algorithms meet regulatory standards for diagnostic accuracy and safety.

Time-to-Treatment Optimization

Analytics dashboard tracks door-to-treatment times, team response metrics, and workflow efficiency. Identifies bottlenecks and measures improvement in time-critical pathways.

Pros & Cons

Advantages

Reduces stroke treatment time by 52 minutes
FDA cleared with breakthrough designation
1400+ hospital deployments
Proven mortality reduction
Comprehensive care coordination
Mobile app for team communication
Multiple time-critical conditions
Real-time imaging analysis
Analytics and quality metrics
24/7 automated monitoring

Disadvantages

Enterprise pricing (significant investment)
Requires CT integration infrastructure
Limited to specific emergency conditions
False positives require physician review
Dependent on imaging quality
Team adoption and training needed
Mobile app requires smartphone access
Best suited for high-volume centers

Pricing Plans

Service	Price	Coverage	Best For
Viz LVO	Custom Enterprise	Stroke detection and coordination	Comprehensive stroke centers, hospitals
Viz PE	Custom Enterprise	Pulmonary embolism detection	Emergency departments, cardiology
Viz Aortic Dissection	Custom Enterprise	Aortic dissection identification	Trauma centers, cardiovascular programs
Viz Platform	Custom Enterprise	Multi-condition package	Healthcare systems, large hospitals

Best Use Cases

Viz.ai Excels At:

Comprehensive Stroke Center Operations: Automating LVO detection and team activation for hospitals performing thrombectomies
Emergency Department Triage: Prioritizing time-critical cases requiring immediate specialist intervention
Telestroke Programs: Connecting remote hospitals with stroke specialists through automated detection and telemedicine coordination
After-Hours Coverage: Ensuring 24/7 stroke detection when specialist presence is limited
Transfer Coordination: Identifying patients at spoke hospitals requiring transfer to thrombectomy-capable centers
Quality Improvement: Tracking and optimizing door-to-treatment times across stroke pathways
Multi-Specialty Emergencies: Coordinating care for pulmonary embolism, aortic dissection, and other time-critical conditions
Care Team Communication: Streamlining emergency specialist activation and coordination through mobile platforms

May Not Be Ideal For:

Small hospitals without thrombectomy capability (unless part of telestroke network)
Facilities with very low stroke volumes
Organizations unable to afford enterprise AI platforms
Settings lacking CT integration infrastructure

How It Compares

Viz.ai vs Traditional Stroke Protocols

Traditional stroke protocols rely on radiologists manually reviewing CT scans and communicating findings to neurologists, who then contact interventionalists if thrombectomy is indicated. This sequential process takes 30-60 minutes or more, during which 1.9 million neurons die every minute in stroke patients. Viz.ai automates detection and parallelizes communication - the AI identifies the LVO within minutes of scan completion and simultaneously alerts the entire team, who can review imaging and prepare for intervention while the patient is still being evaluated. Studies show Viz.ai reduces door-to-treatment time by an average of 52 minutes compared to traditional protocols, translating to significantly improved patient outcomes. For every 15 minutes saved in stroke treatment, one additional patient per hundred achieves independent functional recovery. Viz.ai's automation also ensures consistent 24/7 performance regardless of radiologist availability or fatigue.

Viz.ai vs Other AI Stroke Detection

Several companies offer AI stroke detection, but Viz.ai distinguishes itself through comprehensive care coordination beyond just image analysis. Competitors may detect LVO but lack integrated communication workflows, leaving hospitals to manually notify teams. Viz.ai combines detection with intelligent alerting, mobile care coordination, treatment planning support, and analytics - a complete platform for time-critical emergency response. The 1,400+ hospital deployment demonstrates real-world adoption and trust from healthcare systems. FDA breakthrough device designation and extensive clinical validation provide regulatory and evidence advantages. Viz.ai's continuous expansion to multiple conditions (stroke, PE, aortic dissection) creates a unified platform for time-critical emergencies, while single-condition tools require separate systems. The mobile app's one-touch communication and comprehensive case presentation are particularly valued by clinicians managing emergencies.

Final Verdict

Our Recommendation

Viz.ai represents a transformative advancement in emergency medicine, delivering measurable improvements in treatment speed and patient outcomes for the most time-critical conditions. For comprehensive stroke centers performing thrombectomies, Viz.ai is increasingly becoming standard of care - the ability to reduce door-to-treatment time by nearly an hour translates directly to lives saved and disabilities prevented. The platform's 52-minute average time savings isn't just impressive numbers; it represents patients who walk out of the hospital instead of needing long-term care, families who get their loved ones back, and healthcare systems that can demonstrate superior stroke outcomes. The FDA breakthrough device designation validates Viz.ai's clinical effectiveness, while deployment across 1,400+ hospitals demonstrates real-world adoption and healthcare system confidence. The comprehensive care coordination goes beyond detection - the mobile app transforms emergency team communication, eliminating the phone tag, paging delays, and manual image review that waste precious minutes in stroke emergencies. One-touch calling, complete imaging access, and simultaneous team alerting create the seamless coordination required for optimal time-critical care. Viz.ai's expansion beyond stroke to pulmonary embolism and aortic dissection creates a unified platform for multiple life-threatening emergencies, maximizing return on investment as the system handles more clinical scenarios. The analytics dashboard provides transparency into door-to-treatment times, team response patterns, and workflow bottlenecks, enabling continuous quality improvement and demonstrating value to administrators and payers. For emergency departments, Viz.ai ensures time-critical cases receive immediate specialist attention rather than getting lost in the chaos of busy ERs. For telestroke networks, the platform seamlessly coordinates care between spoke and hub hospitals, identifying transfer candidates and activating receiving teams before the ambulance even departs. The 24/7 automated monitoring means consistent stroke detection regardless of radiologist availability, after-hours coverage, or human fatigue factors that degrade traditional workflows. However, Viz.ai requires significant investment appropriate for high-volume centers or healthcare systems - the enterprise pricing model isn't viable for small hospitals with low stroke volumes unless part of a larger network. The platform requires CT scanner integration and technical infrastructure, representing both financial and IT implementation challenges. Care teams need training on the mobile app and workflow changes, requiring organizational commitment to adoption. False positives necessitate physician oversight - Viz.ai accelerates detection and coordination but doesn't replace clinical judgment. For comprehensive stroke centers, large emergency departments, and healthcare systems seeking to optimize time-critical care pathways, Viz.ai delivers measurable, life-saving improvements justified by the investment. The platform is particularly valuable for organizations with quality improvement initiatives around door-to-treatment times, telestroke programs requiring coordination between facilities, or systems seeking competitive differentiation through superior stroke outcomes. If your hospital performs thrombectomies, manages high-risk pulmonary embolisms, or treats time-critical cardiovascular emergencies, Viz.ai provides capabilities that directly translate to better patient outcomes. The evidence is compelling: faster treatment, reduced mortality, improved functional recovery, and a platform that continues expanding to address more emergency conditions. For organizations where minutes matter and outcomes are measured in lives saved, Viz.ai is an investment that pays dividends in the most important currency - patient lives and quality of life.

Screenshots & Interface

Explore Viz.ai's interface:

Main Interface

Overview of the main interface and features

Frequently Asked Questions

How does Viz.ai detect strokes?+

Viz.ai uses FDA-cleared deep learning algorithms to automatically analyze CT angiography (CTA) scans of the brain, identifying large vessel occlusions (LVO) - blockages in major brain arteries causing severe strokes. The AI was trained on hundreds of thousands of brain scans to recognize patterns of vessel occlusion invisible or time-consuming for humans to detect. When a CTA is performed in the emergency department, images are automatically sent to Viz.ai's cloud servers where algorithms analyze them within minutes. If an LVO is detected with high confidence, the system immediately alerts the stroke team through the mobile app. The algorithms specifically detect occlusions in the internal carotid artery, M1/M2 segments of the middle cerebral artery, and other large vessels - the locations where thrombectomy intervention provides maximum benefit.

How much does Viz.ai cost?+

Viz.ai uses enterprise pricing customized based on hospital size, patient volume, specific products deployed, and contract terms. Pricing typically involves annual subscription fees scaled to the number of CT scanners, emergency department volume, or patient encounters. Comprehensive stroke centers might pay $50,000-150,000+ annually depending on configuration and volume, though exact pricing varies significantly. The investment includes software licensing, mobile app access, cloud computing infrastructure, integration services, training, and ongoing support. Healthcare systems deploying multiple Viz products (stroke, PE, aortic dissection) receive bundled pricing. While substantial, hospitals justify the investment through improved patient outcomes, reduced morbidity/mortality, shorter lengths of stay, and enhanced competitive positioning for stroke care. Many contracts include value-based components tied to time-to-treatment improvements or outcome metrics. Contact Viz.ai directly for detailed pricing appropriate to your specific situation.

Does Viz.ai replace radiologists or neurologists?+

No, Viz.ai augments rather than replaces physicians. The platform accelerates detection and communication but requires physician confirmation and clinical decision-making. When Viz.ai detects a suspected LVO, it alerts the care team, but neurologists review the imaging and clinical presentation before making treatment decisions. Radiologists still provide official interpretations of imaging studies. What Viz.ai eliminates is the time lag between scan completion and specialist notification - instead of waiting for radiologist review, dictation, and communication to neurology, the AI provides immediate flagging allowing parallel workflows. This is particularly valuable after-hours when radiologist or neurologist immediately available. The platform also reduces false negatives - cases where subtle LVOs might be missed by radiologists scanning hundreds of studies. Viz.ai serves as a safety net and acceleration tool, ensuring time-critical cases receive immediate specialist attention while physicians maintain diagnostic authority and treatment decisions.

What are Viz.ai's false positive and false negative rates?+

Viz.ai's FDA clearance studies demonstrated over 90% sensitivity (correctly identifying true LVOs) with specificity around 70-80% (correctly identifying negatives). This means the algorithm catches the vast majority of actual LVOs but generates some false positives - cases flagged as suspicious that turn out not to be true occlusions upon physician review. The balance is intentionally weighted toward high sensitivity because missing a real stroke (false negative) has catastrophic consequences, while false positives only result in brief physician review time. In practice, stroke specialists quickly review flagged cases through the mobile app and dismiss false positives within minutes. The high sensitivity ensures very few true LVOs are missed, providing the safety net hospitals need for time-critical emergencies. False positive rates are acceptable given the alternative - manual review that's slower and subject to human error and fatigue. Continuous algorithm improvements are reducing false positives while maintaining excellent sensitivity.

How quickly does Viz.ai alert the stroke team?+

Viz.ai typically alerts the stroke team within 5-10 minutes of CT scan completion. The process includes: (1) automatic transmission of imaging from CT scanner to Viz.ai cloud, (2) AI analysis of the scan (1-2 minutes), (3) immediate mobile app notifications to all stroke team members simultaneously if LVO detected. This is dramatically faster than traditional workflows where radiologists manually review scans, dictate reports, and communicate findings to neurologists, which can take 30-60+ minutes. The parallel notification means interventionalists, neurologists, and emergency physicians all receive alerts simultaneously rather than sequentially, enabling team preparation while the patient is still undergoing initial assessment. The mobile app provides complete imaging access so specialists can review cases immediately from anywhere. This speed is critical given that stroke patients lose 1.9 million neurons per minute - every minute of delay worsens outcomes.

What conditions beyond stroke can Viz.ai detect?+

Beyond LVO stroke detection, Viz.ai has expanded to identify pulmonary embolism (blood clots in lung arteries) on chest CT scans and aortic dissection (tears in the aorta wall) on CT imaging. The pulmonary embolism module alerts appropriate specialists when high-risk PE is detected, enabling rapid anticoagulation or intervention. The aortic dissection detection flags this life-threatening emergency requiring immediate surgical consultation. Viz.ai continues expanding its portfolio to address additional time-critical conditions where rapid diagnosis and care coordination improve outcomes. The unified platform approach means hospitals can deploy multiple Viz modules through a single integrated system rather than separate point solutions. Future development focuses on other emergency conditions where AI detection and intelligent care coordination can reduce time-to-treatment for life-threatening diagnoses.