Viz.ai
What Is Viz.ai?
Viz.ai is an enterprise clinical AI and care-coordination platform for hospitals. It combines algorithms that analyze medical images with mobile and desktop workflows intended to notify the appropriate care team and help clinicians coordinate a case. The company positions Viz One as the shared platform and says its portfolio includes more than 50 FDA-cleared algorithms. That number is a vendor statement, not a finding that every algorithm is appropriate for every hospital, patient, or workflow.
A separate check of public FDA records for the applicant name “Viz. Ai, Inc.” found 11 510(k) or De Novo entries available at review time, ranging from Viz CTP (K180161, 2018) to Viz Subdural+ (K250354, 2025), including the HCM De Novo record DEN230003. FDA authorization applies to the specific device, version, indications, and labeling in each record; it is not blanket approval of the whole platform.
Platform and clinical product lines
Viz One provides the workflow layer for image intake, case lists, alerts, secure communication, and integrations. Viz.ai organizes its clinical applications into Neuro, Vascular, Cardio, Pulmonary, Trauma, and Oncology lines. Viz Assist adds an AI-assisted interface for finding and navigating clinical information. Exact modules, markets, and authorized indications vary, so buyers should map the contracted product name and version to the applicable regulatory record rather than relying on the portfolio page alone.
A typical hospital workflow
- An imaging study reaches the hospital’s connected systems and an enabled algorithm processes the relevant series.
- If configured criteria are met, the platform surfaces a suspected finding and routes an alert to designated clinicians.
- The care team reviews the original images and patient context, communicates through its approved workflow, and decides the next clinical action.
- Administrators monitor delivery, acknowledgements, escalation rules, and integration failures as part of normal clinical governance.
This is a triage and coordination pattern, not an autonomous diagnosis. Local radiology interpretation, clinical judgment, and existing emergency pathways remain authoritative.
Who it fits — and who it does not
Viz.ai is most relevant to health systems, imaging networks, and specialist service lines with time-sensitive cases and the IT, security, clinical, and procurement staff to govern deployment. It may fit organizations trying to connect detection, notification, and multidisciplinary coordination in one environment. It is not designed for consumers, self-diagnosis, general image recognition, small practices without compatible imaging infrastructure, or teams seeking a stand-alone chatbot.
Practical checks before buying
- Match the label: request the exact module, software version, FDA number, intended use, contraindications, and supported scanner or study requirements.
- Run a silent validation: test retrospectively or in a non-interruptive phase on representative local cases; examine false positives, false negatives, unavailable studies, and subgroup performance before activating alerts.
- Trace the alert path: simulate nights, handoffs, duplicate alerts, network outages, and unreachable clinicians. Define who owns escalation and downtime procedures.
- Verify integration and evidence: confirm PACS/RIS/EHR interfaces, latency measurement, audit export, data retention, model-update controls, and the evidence supporting the exact intended workflow.
- Review the contract: document implementation scope, service levels, training, support, security responsibilities, update notification, data use, and exit or data-export terms.
Security, compliance, pricing, and limits
The Viz.ai Trust Center lists SOC 2 Type II, HIPAA, GDPR, and ISO 27001, 27701, 27799, 27017, and 27018. These are useful diligence inputs, not proof that a customer’s configured deployment is compliant. Hospitals should request current reports and scope, complete their own privacy and security reviews, and execute the required agreements.
Pricing is not public; Viz.ai uses enterprise sales. A meaningful quote should identify modules, sites, study volume assumptions, interfaces, implementation, support, and renewal terms. Algorithm performance can shift with local populations, protocols, scanners, software updates, and workflow design. Alert fatigue, connectivity failures, missing series, and over-reliance remain material risks. No alert should replace review by qualified clinicians, and institutions should deploy each function only within its authorized use and local policy.
Alternatives and useful links
Direct clinical-imaging alternatives include Aidoc and RapidAI, while some hospitals assemble narrower point solutions plus existing PACS and communication tools. Compare authorized indications, local validation results, integration burden, escalation design, and total deployment cost rather than algorithm count alone. For adjacent vertical AI—not a medical substitute—see Harvey AI and AlphaSense to understand how other regulated or evidence-sensitive teams structure human review.