Viz.ai

Viz.ai is a healthcare technology company that develops artificial intelligence solutions for medical imaging, disease detection, clinical decision support, and care coordination. Founded in 2016, the company focuses on helping healthcare organizations identify suspected medical conditions earlier, connect clinical teams, and reduce delays between diagnosis and treatment. Its technology is designed primarily for hospitals, health systems, radiology departments, specialist teams, and life sciences organizations that need to manage complex clinical workflows and improve patient access to appropriate care.

The company’s central product is Viz.ai One®, an AI-powered care coordination platform that brings together medical imaging analysis, clinical information, automated notifications, and communication tools. The platform is designed to help healthcare professionals identify patients who may need urgent attention and share relevant information with the appropriate specialists. Rather than operating as a standalone image-analysis application, Viz.ai One connects detection capabilities with the communication and workflow processes that follow a suspected diagnosis.

Viz.ai’s platform analyzes different types of medical information, including computed tomography (CT) scans, electrocardiograms (EKGs), echocardiograms, and other clinical data. Its AI algorithms can identify imaging patterns associated with selected medical conditions and provide alerts or assessments to support clinical review. The company reports that its platform includes more than 50 FDA-cleared AI algorithms across multiple clinical areas. These applications are designed to help clinicians recognize suspected disease, prioritize cases, and coordinate the next steps in patient care.

One of Viz.ai’s earliest and most established areas is neurovascular care. Its Viz Neuro suite provides AI-supported workflows for conditions involving the brain and blood vessels, particularly stroke. The applications include large vessel occlusion detection, CT perfusion analysis, intracranial hemorrhage detection, cerebral aneurysm assessment, and post-acute stroke care coordination. These capabilities are intended to help clinical teams identify potentially urgent findings and communicate them to stroke specialists more quickly.

Stroke care is especially time-sensitive because treatment decisions may depend on how quickly a patient is assessed and transferred to the appropriate medical team. Viz.ai’s technology can analyze compatible brain imaging, identify suspected abnormalities, and send notifications to relevant clinicians. Specialists can then access the case information through supported mobile or desktop workflows. This approach is intended to reduce communication delays between emergency departments, radiology teams, neurologists, and neurointerventional specialists.

The platform also supports cardiovascular care through Viz Cardio. This suite includes AI applications designed to help identify and manage selected cardiovascular conditions, including hypertrophic cardiomyopathy, acute coronary syndrome, and cardiac amyloidosis. Its tools can analyze relevant clinical information and support the identification of patients who may require further specialist assessment. By connecting detection with clinical communication, Viz.ai aims to help hospitals coordinate cardiovascular care more effectively.

Viz Vascular extends the platform into vascular medicine. Its applications support the identification and management of selected vascular conditions, including aortic disease and pulmonary embolism. These conditions can require prompt clinical assessment and coordination between different specialists. The platform is designed to help healthcare teams receive relevant alerts and access patient information without relying entirely on manual communication between departments.

Another major area is Viz Pulmonary, which focuses on pulmonary care delivery. Its solutions are intended to support the identification and management of selected respiratory conditions by bringing relevant clinical information into a connected workflow. This helps extend the platform beyond neurological and cardiovascular applications and gives healthcare organizations additional options for coordinating care across medical specialties.

Viz Radiology provides AI-supported capabilities designed to help radiologists review medical examinations and manage their workloads. Radiology departments often handle large volumes of imaging studies, and some findings require more immediate attention than others. Viz.ai’s applications can help identify selected abnormalities and communicate relevant findings through compatible radiology workflows. This can support case prioritization and allow radiologists to focus attention on examinations that may require timely review.

The company also offers Viz Trauma, a suite designed to support trauma center workflows. Trauma care often involves multiple specialists who need to assess a patient and coordinate decisions quickly. Viz.ai’s technology is intended to support communication and case management by helping teams access relevant information and respond to suspected injuries. Its purpose is to improve the coordination of clinical teams within trauma care pathways.

Viz Oncology extends the platform into cancer-related care. It is designed to support the identification and coordination of patients who may need oncology assessment or follow-up. This area also connects with Viz.ai’s broader work with pharmaceutical and medical device companies, which use the platform to develop customized solutions for treatable diseases and improve patient access to appropriate therapies.

A significant part of Viz.ai’s offering is its communication and care coordination functionality. Detecting a suspected condition is only one stage of the treatment process. The relevant information must reach the right clinicians, and the care team must be able to coordinate the next steps. Viz.ai One is designed to connect multidisciplinary teams through real-time alerts, mobile communication, and desktop workflows. This can help clinicians share cases, review relevant information, and coordinate patient transfers or specialist consultations.

Viz Connect is another component of the platform. It provides communication and workflow tools intended to connect specialists and care teams across different therapeutic areas. The system can help organize referrals and follow-up processes so that patients identified through clinical workflows are directed toward the appropriate specialists. This is useful in situations where a patient needs continued care after an initial examination or where multiple departments must work together.

Viz.ai also offers Viz Assist, a multimodal AI agent and clinical workflow assistant introduced in October 2025. Viz Assist combines information from electronic health records, ambient listening, and the company’s imaging AI applications to provide clinical teams with a more connected view of a patient’s situation. Its agents are designed to identify potentially important clinical signals, gather relevant patient information, and deliver prioritized insights to healthcare professionals.

Viz Assist can generate summaries using information from connected clinical records and ambient listening workflows. It is intended to reduce the time clinicians spend manually searching through different systems for patient details. The platform can also support clinical documentation and coordination tasks. The company has described plans to expand its capabilities into coding recommendations based on multimodal clinical information. These functions are intended to complement clinical expertise rather than replace the judgement of physicians and other healthcare professionals.

The use of AI agents makes Viz.ai more than a conventional medical imaging analysis platform. Its newer workflow capabilities are designed to operate in the background, monitor relevant clinical information, identify patients who may require attention, and help coordinate tasks across clinical teams. The system can combine imaging findings with information from electronic health records to provide context that may not be available from an image alone. The exact functions available depend on the product, implementation, and clinical environment.

Viz.ai also serves life sciences organizations through customized solutions developed with pharmaceutical and medical device companies. These solutions can support patient identification, clinical trial enrollment, treatment access, patient engagement, and healthcare professional education. By working with hospitals and clinical networks, the company aims to help life sciences partners connect eligible patients with relevant treatment or research opportunities. This business area makes the platform relevant not only to healthcare providers but also to organizations involved in developing and delivering medical therapies.

The platform is designed to integrate with existing hospital technology. Viz.ai supports connections with Picture Archiving and Communication Systems (PACS), Electronic Health Records (EHR), and other clinical infrastructure. Its integration approach uses established healthcare communication standards such as Digital Imaging and Communications in Medicine (DICOM), Health Level Seven (HL7), and Fast Healthcare Interoperability Resources (FHIR). These standards help different healthcare applications exchange medical images, patient information, and clinical messages.

Viz.ai also supports mobile and desktop access, allowing clinicians to receive alerts and review relevant information in different working environments. Its solutions can be embedded within radiology workflows, helping users access AI-generated findings without constantly switching between separate applications. The company provides implementation assistance, onboarding, customer support, and clinical workflow guidance to help healthcare organizations configure the platform for their needs.

Another important aspect of Viz.ai is its clinical validation and research activity. The company highlights clinical studies and publications evaluating the use of its technology in areas such as stroke care, disease detection, communication, and treatment workflows. In January 2026, Viz.ai reported that its platform had more than 120 peer-reviewed publications and abstracts supporting its clinical and operational applications. Such research can help healthcare organizations evaluate the evidence behind specific solutions, although the results of individual studies should be considered according to their methods, populations, and intended clinical uses.

Viz.ai has also reported substantial adoption across healthcare organizations. In January 2026, the company stated that its platform was deployed in nearly 2,000 hospitals across the United States and supported care for more than 230 million people. These figures reflect the company’s reported deployment scale at that time. They also demonstrate its focus on large hospital networks and enterprise healthcare environments rather than individual consumers.

The company works with technology partners to support its cloud and enterprise capabilities. Its announced collaborations include Microsoft and Salesforce, which have been associated with enhancements to its clinical and operational workflows. Viz.ai has also highlighted its work with Microsoft Cloud for Healthcare. These relationships support the broader goal of connecting clinical information, communication, and AI-assisted processes within healthcare organizations.

Security and responsible deployment are important considerations for a platform that processes sensitive medical information. Healthcare organizations must assess data protection, privacy, access control, regulatory requirements, and the security of connected systems before deploying clinical AI. Viz.ai provides enterprise-oriented implementation and support resources, but individual hospitals remain responsible for ensuring that their use of the platform complies with applicable healthcare regulations and internal policies.

It is important to understand that Viz.ai is not designed to replace physicians, radiologists, nurses, or other qualified healthcare professionals. Its algorithms identify suspected findings and provide information that can support clinical assessment and workflow decisions. AI-generated results must still be interpreted by trained professionals within the context of the patient’s symptoms, medical history, examination, and other relevant evidence. Regulatory clearance also applies to specific products and intended uses, not automatically to every capability available through the company’s wider platform.

Viz.ai is primarily intended for enterprise healthcare environments. Hospitals generally need compatible imaging systems, clinical records infrastructure, suitable network connectivity, and staff who can manage the platform within established workflows. Implementation may involve technical integration, clinical validation, onboarding, and coordination between IT administrators and medical departments. These requirements make the platform more suitable for established healthcare organizations than for individual users or small businesses seeking a general-purpose AI application.

Overall, Viz.ai provides a broad healthcare AI ecosystem that combines medical imaging analysis, disease detection, clinical communication, care coordination, and AI-assisted workflow management. Its specialized clinical suites support several medical areas, while Viz Assist expands the platform into multimodal AI agents and documentation support. Its enterprise integrations, clinical research activity, and partnerships make it relevant to hospitals and life sciences organizations seeking to improve the way clinical information is processed and acted upon.

Pricing

Viz.ai uses an enterprise-focused commercial model. The company does not publish standard subscription prices or a public pricing table for Viz.ai One, Viz Assist, or its specialized clinical suites. Healthcare organizations and life sciences companies are directed to contact the company or request a demonstration to discuss their requirements.

Pricing may depend on the products selected, the number of hospitals or facilities involved, the clinical applications required, implementation needs, integration complexity, and the scale of deployment. Organizations should request a tailored quotation that explains licensing, onboarding, technical support, and any additional service costs.

Review

Viz.ai is a comprehensive healthcare AI platform for hospitals and health systems that want to improve disease detection, clinical communication, and care coordination. Its biggest strength is the way it connects AI analysis with the workflows that follow a suspected diagnosis. Rather than simply identifying an abnormality, the platform is designed to notify relevant specialists, share patient information, and support the coordination of treatment decisions.

Its range of clinical applications is another major advantage. The Viz Neuro, Cardio, Vascular, Trauma, Radiology, Pulmonary, and Oncology suites cover several important areas of healthcare. This gives hospitals opportunities to use a connected platform across multiple departments rather than adopting a separate communication system for every clinical application.

Viz Assist adds further value by introducing multimodal AI agents that combine imaging, electronic health record information, and ambient listening. Its ability to summarize patient information and support documentation and coordination tasks could help reduce administrative work for clinical teams. These capabilities also make Viz.ai relevant to the growing use of AI agents in enterprise healthcare workflows.

The platform’s integration approach is useful for hospitals with established technology environments. Support for PACS, EHR, DICOM, HL7, and FHIR can help organizations connect the software to existing clinical systems. Mobile and desktop access also gives healthcare professionals flexibility when reviewing alerts and communicating with colleagues.

Another positive aspect is the company’s emphasis on clinical evidence. Its published research and reported deployment across a large hospital network provide healthcare organizations with material to consider when evaluating the platform. Its enterprise support and implementation services are also valuable for hospitals that need assistance configuring complex clinical workflows.

However, Viz.ai has some limitations. Its pricing is not publicly disclosed, so healthcare organizations must contact the company to obtain a quotation. Enterprise implementation can also require technical resources, staff training, and coordination between clinical and IT departments. These factors may make adoption more difficult for smaller organizations with limited infrastructure.

The platform’s specialized algorithms also have defined clinical purposes. An AI application designed to identify one type of abnormality should not be assumed to detect every possible condition. Hospitals need to evaluate the regulatory status, clinical evidence, and intended use of each application before deployment. AI-generated findings also require professional review and should not be treated as independent medical diagnoses.

Language availability is another point that prospective customers should confirm. Although the company provides English-language product and support information, a comprehensive public list of supported interface and workflow languages is not readily available. Organizations operating in multilingual environments should confirm language support during the purchasing and implementation process.

Overall, Viz.ai is a strong option for large healthcare organizations that need connected clinical AI, medical imaging analysis, and coordinated care workflows. Its broad range of clinical suites, enterprise integrations, and newer AI agent capabilities make it more comprehensive than a single-purpose medical imaging tool. Its custom pricing, implementation requirements, and specialized clinical focus mean that it is best suited to hospitals and health systems with the resources to deploy and manage enterprise healthcare technology.