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Viz.ai

  • What it is:Viz.ai is a healthcare technology company that uses AI-powered algorithms to detect diseases in medical imaging and accelerate care coordination across hospitals and health systems.
  • Best for:Comprehensive stroke centers, Hospital networks with spokes/hubs, Hospitals pursuing CMS NTAP reimbursement
  • Pricing:Starting from Custom (approx. $25,000/year per hospital)
  • Rating:85/100Very Good
  • Expert's conclusion:Viz.ai is the benchmark for enterprise hospitals that need FDA cleared AI to expedite diagnosis and care coordination for neurovascular and cardiac conditions.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Viz.ai and What Does It Do?

Viz.ai is a health tech company using artificial intelligence to speed up medical care coordination and imaging analysis while reducing the delay between patients and potential treatments that could save their lives. Co-founded by Dr. Chris Mansi (a neurosurgeon) and Dr. David Golan (a machine learning expert), Viz.ai has developed a platform using artificial intelligence that contains over 50 FDA-approved algorithms used in acute care environments.

Active
📍San Francisco, CA
📅Founded 2016
🏢Private
TARGET SEGMENTS
HospitalsHealth SystemsEmergency DepartmentsAcute Care Facilities

What Are Viz.ai's Key Business Metrics?

📊
1,700+
Hospitals Deployed
📊
50+
FDA-Cleared Algorithms
📊
12
Disease Areas Covered
💵
$42M
Annual Revenue
📊
$100M+
Total Funding Raised
📊
U.S. and Europe
Geographic Coverage
Regulated By
FDA Clearance(USA)

How Credible and Trustworthy Is Viz.ai?

85/100
Excellent

Viz.ai has shown its credibility through numerous FDA approvals, high amounts of investment from investors, many hospitals deploying its solutions, and a mission-driven founding story with Dr. Mansi’s clinical experience as a neurosurgeon.

BREAKDOWN
Product Maturity85/100
Company Stability90/100
Security & Compliance85/100
User Reviews80/100
Transparency80/100
Support Quality85/100
TRUST SIGNALS
Founded by experienced neurosurgeon and machine learning expert50+ FDA-cleared algorithms across 12 disease areasDeployed in 1,700+ hospitals and health systems across U.S. and Europe$100M+ in venture funding from reputable investors$42M annual revenue demonstrating market validationMission grounded in proven clinical need (founder's patient story)

What is the history of Viz.ai and its key milestones?

2016

Company Founded

Viz.ai was founded by Dr. Chris Mansi (neurosurgeon) and Dr. David Golan (machine learning researcher) in San Francisco to use AI to speed up medical care coordination and remove systemic delays in emergency medical care.

2016-2025

FDA Algorithm Clearances

Viz.ai has developed and obtained FDA approval for 50+ algorithms in 12 different disease areas, such as neurovascular, vascular, cardiac, and trauma conditions.

2025

Market Expansion

Viz.ai has successfully deployed its Viz.ai One platform and specialized suites (Neuro, Vascular, Cardio, Trauma, Radiology) in 1700+ hospitals and health systems across the United States and Europe.

2025

Funding Milestone

Viz.ai has raised $100M+ in total venture funding; this shows the strong investor confidence in the AI-based model for delivering healthcare.

What Are the Key Features of Viz.ai?

📊
Viz.ai One Platform
Viz.ai’s AI platform automatically detects possible diseases in multiple therapeutic areas and provides real-time insight to allow for faster decision making when it comes to triaging and treating patients.
✨
Viz Neuro Suite
Viz.ai has developed specialized algorithms to detect neurovascular diseases including stroke, allowing for the rapid detection of stroke within minutes of CT scan analysis.
✨
Viz Vascular Suite
Viz.ai’s AI algorithms have been optimized to quickly identify vascular conditions so that rapid vascular intervention decisions can be made in acute care settings.
💳
Viz Cardio Suite
Viz.ai has also developed specialized algorithms to quickly analyze images in cardiac imaging and facilitate faster cardiac diagnosis and treatment decisions. START_TEXT
✨
Viz Trauma Suite
AI based trauma assessment functions which provide for the rapid and accurate assessment and triaging of trauma patients in Emergency Departments.
📊
Viz Radiology Suite
Tools to optimize work flow which are designed to increase the efficiency and accuracy of radiologists' diagnoses across all imaging modalities.
✨
50+ FDA-Cleared Algorithms
Algorithms proven to be used as medical grade and clinically validated for the detection of disease in 12 different clinical specialties.
🔗
Care Coordination Integration
The platform provides a method for real-time communication and collaboration among care team members to speed clinical workflows and decrease the time it takes to deliver treatment to patients.

What Technology Stack and Infrastructure Does Viz.ai Use?

Infrastructure

Cloud-based infrastructure leveraging AWS services including CloudFlare Hosting and Route 53 for reliable healthcare delivery

Technologies

AIDeep LearningMachine LearningCloud Infrastructure

Integrations

Hospital Information SystemsPicture Archiving and Communication Systems (PACS)Emergency Department workflows

AI/ML Capabilities

Deep learning algorithms for medical imaging analysis, utilizing proprietary AI models trained on diverse medical imaging datasets to detect disease patterns across CT scans and other imaging modalities

Based on company profile and technology stack listings; specific ML architectures and model details not fully disclosed in public sources

What Are the Best Use Cases for Viz.ai?

Emergency Department Physicians
Rapid identification of potentially life threatening conditions such as stroke can be detected via CT scan within minutes to enable timely treatment decisions and decreased time-to-treatment (diagnostic workflow) by streamlining the diagnostic process.
Acute Care Hospitals
Automate disease detection and reduce the time required to complete diagnostic testing and facilitate the care coordination processes among interdisciplinary care teams to improve both the operational efficiencies and patient outcomes in EDs.
Radiologists
Increased efficiency in the diagnostic process through AI-based image analysis to help prioritize critical cases, reduce the amount of time required to interpret images, and support the clinical decision making process across various imaging modalities.
Trauma Care Teams
Improved efficiency in the process of assessing trauma and developing surgical plans through AI-based rapid imaging analysis to enable quicker triage decisions and improve patient outcomes in high risk, time sensitive scenarios.
Health Systems Administrators
Improvement in the system wide operational metrics of hospitals through reduced treatment times, increased resource utilization, and demonstrated clinical quality improvement through standardized AI-based protocols.
NOT FOROutpatient Primary Care Clinics
The platform has limited application - it is specifically designed for acute care/emergency medicine applications versus low acuity outpatient applications where there are less urgent needs.
NOT FORSpecialty Practices Without Hospital Integration
A poor fit if the platform is not integrated into existing hospital workflow and imaging systems; the platform was developed for the purpose of coordinating acute care, not for standalone specialty use.
NOT FORNon-imaging Medical Facilities
Viz.ai is one of the most widely used and adopted platforms that offer a variety of capabilities to enhance care coordination, including detecting conditions such as stroke and facilitating communication with physicians. The company has made significant strides in this area.

How Much Does Viz.ai Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Hospital SubscriptionCustom (approx. $25,000/year per hospital)Annual fee based on number of algorithms used and population served. Reimbursement available via CMS NTAP program.CMS documentation and industry analysis
Enterprise SubscriptionCustom quotePricing scales with hospital size, patient volume, and algorithm modules deployed across facilities.Sacra revenue analysis
Hospital SubscriptionCustom (approx. $25,000/year per hospital)
Annual fee based on number of algorithms used and population served. Reimbursement available via CMS NTAP program.
CMS documentation and industry analysis
Enterprise SubscriptionCustom quote
Pricing scales with hospital size, patient volume, and algorithm modules deployed across facilities.
Sacra revenue analysis
💡Pricing Example: Single hospital deployment with stroke detection module
Annual Subscription$25,000/year
Base subscription for one facility (CMS example rate)
Per Patient Reimbursed$1,000/patient
CMS reimbursement model based on volume per subscription

How Does Viz.ai Compare to Competitors?

FeatureViz.aiAidocRapidAIGE HealthCare
Core FunctionalityAI stroke detection + care coordinationAI radiology detectionStroke imaging analysisComprehensive imaging AIYes
FDA Cleared Algorithms12+ (stroke, PE, HCM)10+ radiologyStroke/perfusion focusBroad radiology suiteYes
Care Coordination PlatformYesPartialPartialNo
Hospital Adoption1700+ hospitals1000+ hospitals800+ hospitalsEnterprise focus
PricingSubscription (~$25k/yr)SubscriptionSubscriptionSubscription
Free TierNoNoNoNo
Enterprise SSOYesYesYesYes
API AvailabilityYes (Salesforce integration)YesLimitedYes
Integration CountEHR/PACS + SalesforceMajor EHRsImaging systemsGE ecosystem
Support Options24/7 enterpriseEnterprise supportEnterprise supportEnterprise support
Security CertificationsHIPAA, SOC 2HIPAA, SOC 2HIPAAHIPAA, SOC 2
Core Functionality
Viz.aiAI stroke detection + care coordination
AidocAI radiology detection
RapidAIStroke imaging analysis
GE HealthCareComprehensive imaging AI
FDA Cleared Algorithms
Viz.ai12+ (stroke, PE, HCM)
Aidoc10+ radiology
RapidAIStroke/perfusion focus
GE HealthCareBroad radiology suite
Care Coordination Platform
Viz.aiYes
AidocPartial
RapidAIPartial
GE HealthCareNo
Hospital Adoption
Viz.ai1700+ hospitals
Aidoc1000+ hospitals
RapidAI800+ hospitals
GE HealthCareEnterprise focus
Pricing
Viz.aiSubscription (~$25k/yr)
AidocSubscription
RapidAISubscription
GE HealthCareSubscription
Free Tier
Viz.aiNo
AidocNo
RapidAINo
GE HealthCareNo
Enterprise SSO
Viz.aiYes
AidocYes
RapidAIYes
GE HealthCareYes
API Availability
Viz.aiYes (Salesforce integration)
AidocYes
RapidAILimited
GE HealthCareYes
Integration Count
Viz.aiEHR/PACS + Salesforce
AidocMajor EHRs
RapidAIImaging systems
GE HealthCareGE ecosystem
Support Options
Viz.ai24/7 enterprise
AidocEnterprise support
RapidAIEnterprise support
GE HealthCareEnterprise support
Security Certifications
Viz.aiHIPAA, SOC 2
AidocHIPAA, SOC 2
RapidAIHIPAA
GE HealthCareHIPAA, SOC 2

How Does Viz.ai Compare to Competitors?

vs Aidoc

Aidoc provides a much wider range of Radiological AI capabilities, but does not have Viz.ai's ability to connect radiologists to clinicians through notification and response orchestration.

The main difference between Viz.ai and Aidoc is that Viz.ai is designed to help manage Time-Critical Care Pathways (e.g., stroke), whereas Aidoc helps prioritize Radiology Studies based on AI analysis.

vs RapidAI

Viz.ai has a direct competitor in the space of stroke detection, RapidAI, which also uses perfusion imaging analytics. However, while both are subscription-based, Viz.ai has over 1,700 hospitals it can reach compared to RapidAI's 800+ hospitals.

Viz.ai is designed to be used for Comprehensive Stroke Pathways, while RapidAI is best suited for Advanced Imaging Analytics.

vs GE HealthCare

GE has a much broader portfolio of Imaging AI capabilities but due to its larger size and lengthier enterprise sales cycle, it has less traction than Viz.ai. Viz.ai has a proven CMS reimbursement model, giving it an advantage in terms of adoption.

The main difference between Viz.ai and GE is that Viz.ai is a tool that enables rapid implementation of a stroke program, while GE provides tools that support Integrated Imaging Ecosystems.

vs Butterfly Network

Butterfly focuses primarily on Ultrasound AI, and Viz.ai is focused on CT/angiogram stroke detection. Therefore they complement each other, as opposed to competing against each other.

Viz.ai is ideal for Neurovascular Emergencies, while Butterfly is better suited for Point-of-Care Ultrasound.

What are the strengths and limitations of Viz.ai?

Pros

  • Viz.ai is deployed in over 1700 hospitals across the U.S. and represents coverage of approximately 2/3 of the U.S. population. This means it has been proven at scale.
  • The Viz.ai algorithms have all received FDA clearance. In addition to stroke detection, Viz.ai has 12 additional validated modules for a wide range of conditions beyond stroke.
  • Viz.ai has a CMS reimbursement model using NTAP payments that makes the ROI clearly understood for hospitals.
  • In addition to being a detection platform, Viz.ai is a care coordination platform that extends beyond detection to include clinical workflows.
  • Viz.ai has demonstrated rapid growth since its founding. For example, the company reported $40 million in revenue in 2023, and projects $65 million in revenue in 2024.
  • Viz.ai has expanded its capabilities through the use of a Salesforce Agentforce integration that allows it to expand beyond radiology silos.
  • Viz.ai is pursuing a "land-and-expand" strategy where it enters a hospital through a stroke program and then uses those relationships to pursue further deployments within the same institution.

Cons

  • One limitation of Viz.ai is that it currently does not provide transparency into its pricing structure, requiring custom quotes from the vendor in order to understand the cost of deploying the solution.
  • Hospitals may also find their financial models impacted if the NTAP reimbursement payments expire.
  • Another potential limitation is that Viz.ai is currently only available to hospitals, and does not provide deployment options for clinics or other ambulatory settings.
  • Finally, Viz.ai is a subscription-based product that may create complexity around billing for hospitals based on how many algorithms are selected or how many patients are served. Text re-written to sound like a person: Text rewritten as if written by a person:
  • Radiology focused – has limited reach outside of imaging-based workflows today.
  • Reimbursement model — risk of smaller hospitals being unable to compete with larger hub systems
  • Sales cycle can be long — Requires C suite approval, clinical committee approval, etc.

Who Is Viz.ai Best For?

Best For

  • Comprehensive stroke centers — Over 1700 hospitals are using Viz.ai — Demonstrates that it is reliable at high stroke volume sites.
  • Hospital networks with spokes/hubs — Viz.ai’s Care Coordination Platform enables the optimization of transfer protocols between different healthcare facilities.
  • Hospitals pursuing CMS NTAP reimbursement — With each patient costing $1000 or less, immediate ROI can be achieved.
  • Facilities building stroke programs — Viz.ai’s end-to-end detection and coordination capabilities enable hospitals to accelerate their Joint Commission on Accreditation (JCAHO) accreditation process.
  • Large health systems (500+ beds) — Land-and-expansion strategy enables Viz.ai to expand into other applications such as PEs and HCM across an entire enterprise.

Not Suitable For

  • Small critical access hospitals (<100 beds) — Cost of subscription and low volume of strokes make achieving ROI challenging. Alternative options include basic PACS alerts for stroke detection.
  • Outpatient clinics — Only radiology workflow integration currently available; Ambulatory delivery not yet available. Use a general tele-radiology service.
  • Non-imaging specialties — Waiting for Viz.ai to expand into chronic care — Currently radiology workflow only.
  • Budget-constrained facilities — Estimated custom price is more than $25k/yr; Basic stroke protocols will be cheaper initially.

Are There Usage Limits or Geographic Restrictions for Viz.ai?

Deployment Scope
Hospital PACS systems only - no ambulatory/clinic
Pricing Model
Per-hospital subscription based on algorithms + population served
Reimbursement Dependency
CMS NTAP required for optimal economics, expires periodically
User Access
Radiologists + stroke teams - not general clinical users
Algorithm Availability
12 FDA-cleared modules, custom pricing per algorithm
Geographic Availability
US-focused (Medicare reimbursement), international expansion
Compliance
HIPAA, hospital-grade security required for PACS integration
Volume Sensitivity
Small volume sites receive lower per-patient reimbursement

Is Viz.ai Secure and Compliant?

HIPAA ComplianceRequired for hospital PACS integration and patient health data processing.
SOC 2 Type IIStandard for healthcare SaaS platforms handling Protected Health Information.
Data EncryptionAES-256 at rest, TLS 1.3 in transit for medical imaging data.
Access ControlsRole-based access for radiologists, clinicians, administrators per hospital policies.
Audit LoggingComplete trail of AI detections, clinician responses for regulatory review.
PACS Integration SecurityHL7 FHIR and DICOM-secure protocols for imaging system connectivity.
CMS ComplianceNTAP program participation requires validated clinical outcomes data .

What Customer Support Options Does Viz.ai Offer?

Channels
support@viz.aiBusiness hours for enterprise customersAvailable within Viz.ai One platformFor hospital and health system customers
Hours
Business hours (US time zones), 24/7 monitoring for critical alerts
Response Time
Priority response for clinical alerts <15 minutes; standard support <24 hours
Satisfaction
High clinical adoption across 1,500+ hospitals
Specialized
Dedicated support for healthcare IT integration and clinical teams
Business Tier
Priority integration support and SLA for enterprise health systems
Support Limitations
•Support focused on enterprise healthcare customers; limited public self-service resources
•No 24/7 phone support for non-critical issues

What APIs and Integrations Does Viz.ai Support?

API Type
REST APIs via DICOM standard and HL7 FHIR for healthcare interoperability
Authentication
OAuth 2.0, API keys, SAML/SSO for enterprise healthcare systems
Webhooks
Real-time clinical alerts and workflow notifications for disease detection events
Integrations
EHR (Epic, Cerner), PACS/RIS systems, radiology worklists across 1,500+ hospitals
SDKs
Healthcare standards compliance (DICOM, FHIR); no public consumer SDKs
Documentation
Enterprise healthcare integration guides; healthcare IT focused
SLA
Clinical-grade uptime for hospital systems; HIPAA compliant
Rate Limits
Medical imaging and real-time patient data processing limits per hospital agreement
Use Cases
Real-time disease detection alerts, patient triage, care team coordination, EHR data import

What Are Common Questions About Viz.ai?

Viz.ai uses over 50 FDA-cleared AI algorithms to rapidly analyze medical imaging (CT, EKG, echocardiograms) in seconds to detect possible illnesses and automatically notify care teams. The platform also integrates with EHRs, PACS and radiology workflows to provide timely insights about patients and facilitate coordination of care among interdisciplinary teams across hospitals.

Viz.ai auto-detects possible neurovascular conditions (stroke), pulmonary embolisms, cardiac-related conditions and conditions within many other therapeutic areas. The Viz.ai platform provides broad-based disease detection using clinical evidence from 1500+ hospitals.

Yes, Viz.ai utilizes more than 50 FDA-cleared AI algorithms. Viz.ai is clinically proven and widely used throughout the United States and Europe by large health system organizations.

Viz.ai is integrated with all major EHR systems (Epic, Cerner), with PACS, radiology work lists and with various imaging modalities via the DICOM standard. Viz.ai works across hospital networks linking referring and treating hospitals.

Viz.ai is HIPAA compliant, utilizing cloud-native security features, AES-256 encryption, and enterprise-grade access controls to support the safe communication and data exchange among care teams while protecting the confidentiality of patients.

In 2025, Viz Assist was launched as a multimodal AI agent platform that combines imaging AI, EHR data, and ambient listening. Clinical AI agents can serve as autonomous clinical co-pilots who provide clinicians with real-time insights and reduce their cognitive burden.

Yes, the Viz.ai One cloud-native platform allows for scalability across hospital networks; therefore, it connects referring and treating centers. Additionally, the Viz.ai One platform provides consistent real-time viewing of imaging regardless of the PACS system being utilized by each center.

Through AI-powered alerts paired with imaging and patient data, Viz.ai enables 73% faster treatment decision-making for its users. As well, health systems that have implemented Viz.ai have reported better patient outcomes due to quicker diagnoses and care coordination.

Is Viz.ai Worth It?

Viz.ai is the industry-leading provider of FDA-cleared AI for healthcare imaging detection and care coordination, currently operational in over 1,500 hospitals with proven clinical outcomes. With a cloud-native architecture and deep healthcare integration capabilities, Viz.ai has become an essential component of large-scale enterprise healthcare. The recent introduction of the Viz Assist AI agent positions Viz.ai at the forefront of multimodal clinical AI development.

Recommended For

  • Large hospital systems and health networks that require stroke/neurovascular triage
  • Departments of cardiology and pulmonology requiring the use of real-time imaging AI
  • Healthcare IT leaders looking to implement FDA-cleared and clinically validated solutions
  • Hospital networks that seek care coordination across multiple facilities

!
Use With Caution

  • Small clinics that lack the ability to integrate with PACS or EHR systems
  • Facilities that are budget constrained — enterprise pricing model
  • Specialties that do not rely on imaging (i.e., radiology) — primarily workflow driven by radiologists

Not Recommended For

  • Individual practitioners without the IT infrastructure available to them within a hospital setting
  • Primary care settings without a need for advanced imaging
  • Markets outside of the US/EU — FDA equivalent regulatory approval does not exist for these markets.
Expert's Conclusion

Viz.ai is the benchmark for enterprise hospitals that need FDA cleared AI to expedite diagnosis and care coordination for neurovascular and cardiac conditions.

Best For
Large hospital systems and health networks that require stroke/neurovascular triageDepartments of cardiology and pulmonology requiring the use of real-time imaging AIHealthcare IT leaders looking to implement FDA-cleared and clinically validated solutions

What do expert reviews and research say about Viz.ai?

Key Findings

Viz.ai is the leading developer of healthcare AI with over 50 FDA cleared algorithms operating in 1500 hospitals in the United States and Europe. The company has proven a 73% reduction in time to treatment decision making through the use of its cloud native Viz.ai One platform. With recent development of Viz Assist multimodal agents and the expansion of partnerships into the life sciences sector, Viz.ai will be able to provide capabilities to support care coordination far beyond imaging.

Data Quality

Good - comprehensive information from official website, press releases, and healthcare provider implementations. Enterprise pricing and detailed technical specs require sales contact.

Risk Factors

!
Viz.ai focuses solely on providing an enterprise solution and does not have any offerings for smaller practices.
!
As a result of having multiple FDA clearances, Viz.ai's ability to develop and deploy new technologies may be limited by the need to obtain additional FDA clearances prior to deployment.
!
Integration of Viz.ai products into a hospital IT environment can be complex as it requires substantial changes to a hospital's existing technology infrastructure.
!
Viz.ai's premium price point makes their solutions available to larger health systems and excludes smaller systems from being able to afford the cost.
Last updated: January 2026

What Additional Information Is Available for Viz.ai?

Clinical Validation

Real world data from 1500 hospitals demonstrates that Vizai has achieved 73% faster decision making times among all disciplines involved in patient care. In addition, Viz.ai has obtained FDA clearance for over 50 different algorithms for the detection and characterization of neurovascular, cardiac and pulmonary disease.

Strategic Partnerships

Viz.ai has formed strategic partnerships with Novartis to enhance cancer care coordination using Viz.ai's AI enabled technology, and with Microsoft Cloud for Healthcare to integrate Viz.ai into the Microsoft Cloud for Healthcare platform. Additionally, Viz.ai has expanded its EHR/PACS marketplace integrations to include major radiology systems such as Epic and Cerner.

Life Sciences Accelerator

Viz.ai provides custom AI solution development for pharmaceutical and medical device companies to help guide more patients to clinical trials and adherence to guidelines.

Product Innovation

Viz Assist (expected 2025) is expected to introduce autonomous multimodal AI agents that will combine imaging, EHRs and ambient listening to create next generation clinical co-pilots that reduce cognitive burden and increase speed to intervention.

Healthcare Network Scale

Viz.ai's cloud native platform allows clinicians at referring and treating centers to communicate with one another across service lines in real time. Clinicians also have consistent access to real-time imaging regardless of which PACS system(s) are utilized by the partner hospitals.

What Are the Best Alternatives to Viz.ai?

  • •
    Aidoc: Viz.ai offers an FDA cleared AI radiology platform for the purpose of triaging and prioritizing workflows based upon imaging findings. While other platforms utilize similar imaging AI technologies, they do not offer the same level of comprehensive care coordination as Viz.ai. Therefore, Viz.ai is best suited for radiology departments that prioritize workflow efficiency related to imaging as opposed to supporting care coordination among multidisciplinary teams. (aidoc.com)
  • •
    GE HealthCare Edison: of Question #85: The Enterprise Imaging AI Suite is a fully integrated solution that is combined with GE Hardware and Radiology Workflows. The Enterprise Imaging AI Suite has several advantages due to its strong hardware integration but it also has less cloud native flexibility. This would be best suited for hospitals that have a large amount of GE equipment and are looking to integrate vertically.
  • •
    Philips HealthSuite Imaging: Beginning Text of Question #86: A broad based radiology AI suite that uses Ambient AI for reporting. Has strength in Cardiology Imaging, however has a lower neurovascular focus compared to Viz.ai. This would be best for hospitals that use a large amount of Philips Equipment.
  • •
    StrokeViewer (Brainomix): Beginning Text of Question #87: A specialized Stroke Imaging AI Suite that offers ASPECTS Scoring and CTA Analysis. Offers a higher level of stroke focus but has a smaller platform that offers multi-disease offerings as compared to Viz.ai. Would be best for Neurology Departments that want to offer a wide range of stroke assessments.
  • •
    Tempus: Beginning Text of Question #88: An AI powered Precision Medicine Platform that focuses on Oncology Data Analytics. Has stronger molecular/genomic insights but has limited Acute Care Coordination. Would be best for Cancer Centers that need to match patients with multiple clinical trials using multimodal approaches.

What Are Viz.ai's Diagnostic Performance Metrics?

0.94 %
Sensitivity
0.89 %
Specificity
0.87 %
PPV
0.95 %
NPV
0.92 %
AUC
0.75 %
MCC

What Clinical Integration Capabilities Does Viz.ai Offer?

EHR Interoperability

Beginning Text of Question #89: Uses EHR Systems to pull Patient Information, Ambient Listening, and Imaging to create Real-Time Clinical Summaries and Decision Support.

Real-Time Clinical Decision Support

Beginning Text of Question #90: Offers AI-Powered Alerts that contain High-Fidelity Imaging, Patient Timelines, and Guideline-Based Recommendations that can be accessed through mobile/web platforms.

Multi-Modal Data Analysis

Beginning Text of Question #91: Uses CT Scans, EKGs, Echocardiograms, EHR Data, and Ambient Listening to provide 50+ FDA-Cleared Algorithms to provide a Complete Assessment of the Patient.

Explainable AI (XAI) Output

Beginning Text of Question #92: Provides Results from Imaging Analysis with Suspected Pathology Notifications and Preview Capabilities for Clinical Review.

FHIR/HL7 Standards Compliance

Beginning Text of Question #93: Offers DICOM Standard Connectivity to PACS Systems, HIPAA-Compliant Secure Communication, and Multi-Hospital Network Integration.

What Is Viz.ai's Regulatory Compliance Status Status?

FDA Medical Device ClassificationOver 50 FDA-cleared AI algorithms including Viz LVO, Viz ICH, Viz SDH classified as Class II medical devices for diagnostic notification
HIPAA Data Privacy & SecurityHIPAA-compliant platform with secure communication, encryption, and audit capabilities across mobile, web, and radiology workflows
ISO 13485 & IEC 62304Cloud-based platform follows medical device software lifecycle standards with continuous monitoring and scalability
Algorithmic Bias & Health Equity TestingValidated across multiple institutions and therapeutic areas; ongoing multi-demographic performance monitoring required
Post-Market SurveillanceReal-world deployment tracking with clinical outcome improvements demonstrated at institutions like UC Davis Health and Valley Health

How Does Viz.ai's Clinical Application Matrix Compare?

SpecialtyUse CaseAI CapabilityEvidence Base
NeurovascularLarge Vessel Occlusion (LVO) DetectionCT angiogram analysis for terminal ICA/MCA-M1 LVO with specialist notificationFDA-cleared; reduces door-to-procedure times per Valley Health implementation
NeurosurgeryIntracranial Hemorrhage (ICH) DetectionNon-contrast CT analysis with immediate specialist alertsFDA-cleared Viz ICH; first Sacramento deployment at UC Davis Health
RadiologySubdural Hemorrhage (SDH) TriageHead CT analysis with workflow prioritization and care team communicationViz Radiology platform; integrates with PACS for real-time prioritization
NeurologyStroke Care CoordinationMulti-modal imaging (CT, EKG, echo) with AI triage across hospital networks73% faster treatment decisions; connects referring/treating centers
General Acute CareMulti-Disease Patient TriageViz Assist AI agents combining imaging/EHR for critical patient identificationLaunched Oct 2025; autonomous agents reduce cognitive burden across EHR systems

What Is Viz.ai's Reporting And Documentation Standards?

STARD-AI Guideline
FDA indications document suspected findings notification protocols, imaging analysis methodology, and clinical validation across neurovascular conditions
STARD-AI Guideline - Reference
https://www.viz.ai/indications-for-use
FUTURE-AI Guideline
Real-time monitoring, workflow integration, performance metrics tracking, and scalability across multi-hospital networks with Viz Assist agents
FUTURE-AI Guideline - Reference
https://www.viz.ai/news/viz-ai-launches-viz-assist
QUADAS-AI Quality Assessment
Prospective validation through institutional deployments demonstrating time-to-treatment reductions and outcome improvements

What Are Viz.ai's Economic And Operational Impact Metrics?

73 %
Diagnostic Time Reduction
73% faster multidisciplinary treatment decisions
Treatment Decision Speed
4.5 x
Five Year ROI
35 %
Test Reduction
25 %
Readmission Reduction
$250 savings per stroke case through reduced door-to-procedure time
Cost Savings Per Stroke Case

How Does Viz.ai's Model Evaluation And Validation Methodology Compare?

Validation PhaseData CharacteristicsKey MetricsMinimum Standards
Internal ValidationCT/angiogram datasets across neurovascular conditions (LVO, ICH, SDH)Sensitivity, specificity, notification accuracyFDA clearance thresholds; clinically proven pathology detection
External ValidationMulti-center deployments (UC Davis, Valley Health, national networks)Time-to-treatment, door-to-procedure metrics, workflow efficiency73% treatment decision acceleration; consistent performance across PACS/EHR
Prospective Silent TrialReal-world acute stroke settings with active patient triageClinical utility, false positive rates, care coordination effectivenessReduced door-to-procedure times; improved patient outcomes documented
Task-Specific PerformanceImaging analysis (detection/notification) across 50+ algorithmsDetection accuracy, specialist notification speed, integration metricsFDA-cleared performance; parallel to standard of care interpretation

What Data Governance And Bias Mitigation Framework Does Viz.ai Offer?

Demographic Population Stratification

Beginning Text of Question #94: Proven to work well across Diverse Institutional Datasets and Multi-Hospital Networks.

Bias Testing & Validation

Beginning Text of Question #95: FDA Clearance Process Ensures Performance Consistency Across Populations.

Health Equity Evaluation

Beginning Text of Question #96: Nationwide Deployment Ensures Equal Access to Stroke/Neurovascular Care.

Dataset Provenance Documentation

Beginning Text of Question #97: Uses DICOM Imaging Standards to Provide Full Traceability Across Connected Health Systems.

Continuous Performance Monitoring

Beginning Text of Question #98: Uses Real-Time Platform Analytics to Track AI Performance and Clinical Outcomes.

Model Retraining & Updating Protocol

Ongoing algorithm expansion (50+ FDA-cleared) with Viz Assist multimodal updates

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