Best Stroke AI Software for Hospitals: RapidAI vs Viz.ai Compared

The short answer: The two stroke AI platforms US hospitals actually shortlist are RapidAI and Viz.ai, and as of 2026 there is finally a peer-reviewed head-to-head between them. The DUEL study, published in the American Journal of Neuroradiology in May 2026, ran both tools side by side on 1,589 consecutive code strokes at one comprehensive stroke center and reported RapidAI detecting 98% of confirmed large vessel occlusions against 73% for Viz.ai. That result matters, but it is one retrospective single-center study publicized by the winning vendor, and it measures LVO detection only. Viz.ai holds the broader regulatory portfolio, with 50-plus FDA clearances spanning stroke, hemorrhage, aneurysm, pulmonary embolism, aortic and cardiac conditions, and its strength has always been care coordination rather than detection alone. Neither vendor publishes list pricing.

Updated August 2026. Clearances and product modules in this category change several times a year, so confirm the current list with each vendor before building a shortlist around this article.

Stroke AI is the one imaging AI category where a hospital can draw a straight line from software to outcome. An LVO that gets seen twenty minutes earlier is a thrombectomy that starts earlier, and the clinical literature on time to treatment is not ambiguous. That makes the buying decision unusually consequential and unusually hard, because for years both leading vendors could point at their own favorable real-world data and no one had run them against each other.

That changed in 2026. Below is what the head-to-head actually found, what it does not prove, and the questions worth asking before a comprehensive stroke center signs anything.

RapidAI vs Viz.ai: the DUEL study head to head

The DUEL study, formally titled "Detection of Large Vessel Occlusion Using AI: Evaluating Performance of RapidAI LVO vs Viz.ai LVO in 1,589 Consecutive Code Strokes," was published online in the American Journal of Neuroradiology in May 2026 by Sachdev, Hudson, Ong, Marklein and Flores. Both tools ran simultaneously on the same consecutive, unselected stroke alerts at the same institution over roughly two years, which removes the case-selection problem that makes most vendor-published studies hard to compare.

Source: American Journal of Neuroradiology, published online May 2026, and the accompanying vendor announcement. Compiled August 2026.

Two numbers in that table deserve more attention than the headline sensitivity figure. The first is the processing rate. RapidAI returned a result on 99.9% of studies, Viz.ai on 90%. A tool that silently fails to process one study in ten is a different operational problem from a tool that processes everything and occasionally gets it wrong, because the second failure is visible to your team and the first is not. Ask both vendors how a processing failure surfaces in your workflow and whether anyone is paged when it happens.

The second is specificity, where the gap is small: 94% against 91%. On a large code stroke volume, a three-point specificity difference still produces a meaningful number of extra false activations, and false positives are what erode neurointerventionalist trust in an alerting system. Neither vendor looks bad here.

What the DUEL study does not prove

Take this result seriously and take it in context. Four limitations are worth holding onto.

It is a single-center study. The authors say so themselves. Scanner models, CTA acquisition protocols, contrast timing and patient mix all vary between institutions, and all of them affect algorithm performance. A result generated on one center's protocols is evidence, not a guarantee of what you would see on yours.

It was publicized by the vendor that won. RapidAI promoted the publication and issued a press release framing the outcome as 33% more LVO cases identified than the leading competitor. That does not make a peer-reviewed AJNR paper wrong, and independent authorship matters more than who tweeted it. It does mean you should expect Viz.ai to contest the methodology, and you should ask them to, directly, in the sales conversation.

It measures one module. This is LVO detection on CTA. It says nothing about intracranial hemorrhage detection, aneurysm surveillance, perfusion analysis, or the care coordination layer that sits on top of any of them. A hospital buying a stroke platform is rarely buying only LVO detection.

It is retrospective. The tools were evaluated against a confirmed ground truth after the fact, not in the live decision path with clinicians acting on the output. Real-world impact studies, which both vendors have published, measure something different and are also worth reading.

Where Viz.ai is genuinely stronger

An honest comparison has to state this plainly, because the DUEL headline invites the conclusion that the decision is settled. It is not.

Viz.ai holds the broader regulatory portfolio by a wide margin, with more than 50 FDA 510(k) clearances covering stroke, hemorrhage, aneurysm, pulmonary embolism, aortic disease, cardiac conditions and subdural hematoma. It was also first to market in this category, and its care coordination platform, the mobile application that pulls the whole stroke team into one thread with the images attached, is the part customers most consistently describe as the product's real value. Viz.ai has its own published real-world evidence too, including the VALIDATE study in Frontiers in Stroke on reducing delays to endovascular treatment.

If your bottleneck is not detection but the twelve minutes lost between a finding and the interventionalist seeing it, that coordination layer is the thing you are buying, and detection sensitivity is only one input to it.

RapidAI, for its part, has been expanding fast beyond stroke. In November 2025 it received FDA clearance for five additional modules: Rapid DeltaFuse for automated alignment of serial non-contrast head CTs, Rapid LMVO extending coverage to distal and posterior territories, Rapid MLS for midline shift quantification with a stated mean absolute error of 0.8 mm, Rapid OH for suspected obstructive hydrocephalus, and Rapid Aortic for aortic measurement.

Which is better for LVO detection, RapidAI or Viz.ai?

On the only published head-to-head evidence available, RapidAI. The DUEL study found 98% sensitivity against 73% on 1,589 consecutive code strokes at the same center, with a higher processing rate and slightly better specificity. That is the strongest comparative evidence in the category today. It remains one retrospective single-center study, so treat it as a strong reason to demand a local validation period rather than as a settled verdict.

How much does stroke AI software cost per year?

Neither RapidAI nor Viz.ai publishes list pricing, so any specific figure you read online is someone's guess. Contracts in this category are typically annual subscriptions scoped by site and by module, which means the number moves substantially depending on how many algorithms you switch on. Practical advice: get quotes priced per module rather than as a bundle, so you can see what each capability actually costs and drop the ones your team will not use. The wider pattern of what radiology software costs, drawn from public federal contract awards, is covered in our radiology software pricing research.

Do we need stroke AI if we already have a PACS?

They solve different problems. A PACS stores and displays the study and shows it to whoever opens it. Stroke AI runs an algorithm the moment the CTA reconstructs and pushes an alert to the people who can act, often before the study has been formally read. The value is in the notification path, not the storage. If you are working out which layer of your imaging stack you are actually replacing or adding to, our guides to PACS software and enterprise imaging lay the layers out side by side.

Does stroke AI replace the radiologist read?

No, and no vendor in this category claims it does. These are triage and notification tools cleared to flag suspected findings for prioritized review. The formal interpretation still belongs to the radiologist, and the alert is a prompt to look sooner rather than a diagnosis. The FDA clearance pathway for most of these products is computer-aided triage, which is specifically not a diagnostic claim. We cover what that distinction means in practice in is radiology AI FDA cleared.

What should a hospital ask before signing

Six questions that reliably separate a good stroke AI deployment from an expensive one.

Ask for a local validation period before full commitment. Run the tool against your own consecutive alerts and your own confirmed outcomes for a defined window. Both vendors will resist a formal head-to-head; ask anyway, and at minimum negotiate an exit if measured local sensitivity falls below a stated threshold.

Ask what happens when a study fails to process. The DUEL processing-rate gap makes this concrete. Silent failure is the dangerous mode. You want a visible queue of unprocessed studies and someone accountable for it.

Ask how the alert reaches the on-call team, and what monitors that path. The clinical benefit lives entirely in a mobile notification arriving within seconds. That path crosses your network, a vendor cloud and a push notification service, and if any link degrades at 3am nobody finds out from the software itself. Hospitals running this well treat the alert endpoint as production infrastructure and put independent uptime monitoring on it rather than trusting the vendor dashboard alone.

Ask for per-module pricing and real utilization data. Bundles hide modules nobody opens. After a year, ask the vendor how many times each algorithm actually fired at your site.

Ask about integration with the reading workflow, not just the phone. An alert that never lands in the radiologist's worklist creates a parallel workflow. Whether flagged studies reorder the reading list is a genuine capability question, covered further in worklist prioritization and AI triage in radiology.

Ask how many vendors you are willing to run. Stroke AI is usually the first imaging AI a hospital buys and rarely the last, and each additional vendor adds an integration, a contract and a security review. We worked through the tradeoff in how many radiology AI vendors a practice needs.

How to choose between them

Two clean decision rules, based on what the evidence actually supports.

If LVO detection sensitivity is your primary constraint, and particularly if your team has lost confidence after known misses, the DUEL data points at RapidAI and you should ask Viz.ai to respond to it directly with their own comparative evidence.

If your bottleneck is coordination rather than detection, or if you want one vendor covering stroke plus pulmonary embolism, aortic and cardiac pathways under a single contract and security review, Viz.ai's breadth is a real advantage that the DUEL study does not measure at all.

Either way, insist on a local validation window. The single most useful thing in the DUEL paper is not which vendor won. It is the demonstration that running two algorithms against your own consecutive cases and counting the misses is achievable at a normal stroke center, which means you can measure this yourself instead of arbitrating between two vendors' marketing.

For the wider view of which imaging AI vendors serve which purpose, our best AI radiology software roundup covers the full category, and the RapidAI comparison and Viz.ai comparison go deeper on each vendor individually. If you are early in the process, the vendor evaluation questions we use are a reasonable starting checklist.