Diagnostics & imaging AI · Stroke imaging AI
Brainomix
Brainomix makes AI imaging software for stroke and lung disease. Brainomix 360 Stroke reads CT and MRI scans to support treatment and transfer decisions, and e-Lung measures lung fibrosis on CT.
Strong fit
- Regional and national stroke networks that want proof from large real-world studies
- Hospitals that rely on non-contrast CT and CT angiography and have few stroke specialists on site
- Health systems that also want AI for lung fibrosis scans
Weak fit
- Hospitals that want a single vendor for every imaging AI use
- Teams that mainly need a messaging app for the care team
- Buyers who need a published price list
Bottom line
Brainomix is an Oxford, England company that spun out of the University of Oxford. Its Brainomix 360 Stroke software reads stroke scans, including non-contrast CT, CT angiography, CT perfusion, and MRI, and helps doctors decide quickly who needs treatment or transfer. It suits networks that want evidence from large real-world studies, especially for hospitals with few stroke specialists.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Brainomix was founded as a University of Oxford spinout by academics including Alastair Buchan, Oxford's professor of stroke medicine, and co-founder Michalis Papadakis is its chief executive. The company dates its start to 2012, and in 2016 it launched e-ASPECTS, which it calls the first fully automated AI imaging tool. Its Brainomix 360 Stroke software scores early stroke damage on non-contrast CT, finds large blocked arteries on CT angiography, and maps blood flow on CT perfusion and MRI. Brainomix later applied the same approach to lung fibrosis, and its e-Lung software is cleared by the FDA. In 2021 Brainomix 360 Stroke was deployed nationally in Hungary and Wales.
Brainomix raised a £14 million ($18 million) Series C in March 2025, co-led by Parkwalk and the Boehringer Ingelheim Venture Fund, and in February 2026 extended the round to $25.4 million. It says its software has been deployed to more than 300 hospitals in more than 20 countries, and it runs its U.S. business from Chicago. A study in The Lancet Digital Health covered more than 450,000 patients at 107 NHS England hospitals over five years. The 26 hospitals using Brainomix 360 Stroke saw thrombectomy rates rise 100 percent, compared with 63 percent at the other hospitals.
Compare RapidAI when a comprehensive stroke center wants the platform used in the late-window treatment trials, Viz.ai when coordinating the care team matters most, Nicolab when hospitals need to share images and messages across a network, and Methinks AI when many hospitals do only non-contrast CT.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| RapidAI | 8.1 | 7.6 |
| Brainomix | 7.8 | 7.7 |
| Viz.ai | 7.7 | 7.3 |
| Nicolab | 7.3 | 7.3 |
| Methinks AI | 6.8 | 7.0 |
Pricing
| Item | Detail |
|---|---|
| Model | Subscription quoted by Brainomix, usually by hospital or network, with national and regional contracts in Europe. |
| What usually drives cost | Number of hospitals, which scan types are analyzed, Vantage network analytics, and whether e-Lung is added. |
| What to ask in diligence | Annual price per hospital, the cost of adding spoke hospitals, and what network analytics cost. |
| Published pricing | No public list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Brainomix to function | |
| Stroke network lead | Hub and spoke hospitals need one owner. |
| CT and MRI connections at every site | Each scanner must send studies automatically. |
| Transfer protocol | Results should trigger an agreed transfer decision. |
| Information governance approval | Images leave the hospital for analysis. |
| Baseline treatment rates | Needed to show change later. |
| What will maximize your value | |
| Roll out to spoke hospitals first | The Lancet study saw most gains at primary stroke centers. |
| Use Vantage analytics to review delays | Shows where transfers stall. |
| Train emergency doctors, not only radiologists | Decisions start in the emergency department. |
| Deal-breakers | |
| No network lead | |
| No baseline data | |
| Wants one vendor for all imaging AI | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract and information governance | 4-8 weeks |
| 2 | First hospitals live | 1-2 months |
| 3 | Network rollout | 3-9 months |
Leadership
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.