Harrison.ai
Clinical imaging AI that gives radiologists an assistive second read on chest X-ray, CT brain, and CT chest studies.
If your department reads high volumes of chest X-ray and head CT, Harrison.ai can cover several routine study types under one procurement and one integration — a narrower shortlist than stitching together point tools. The disclosed adoption numbers carry the pitch: more than a third of NHS England chest X-rays is production scale, not a pilot. Where Aidoc and Viz.ai lean into triage and care-coordination handoffs, Harrison.ai's case rests on breadth of findings across three modalities plus worklist prioritisation. Go in knowing it's an infrastructure project with clinical governance attached, not something you switch on in an afternoon.
Verified 4d ago · liveness 60/100 · cite: rightaichoice.com/tools/harrison-ai
- High-volume hospital radiology departments reading chest X-ray and head CT at scale
- Public health systems and large private hospital groups standardising imaging AI across sites
- Teleradiology providers that need consistent finding coverage across many referring sites
- Emergency and acute care programmes where faster time to diagnosis is the core metric
- Low-volume private practices where integration overhead outweighs caseload
- Buyers whose bottleneck is a single sub-specialty exam outside chest X-ray, CT brain, or CT chest
- Organisations without imaging IT or PACS/RIS integration capacity
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Skip Harrison.ai if your reading volume is concentrated in a single sub-specialty exam outside chest X-ray, head CT or chest CT, or if you lack the imaging IT team to run a health-system deployment.
Harrison.ai sells to healthcare organisations and does not publish a tier list, so the comparison that matters isn't price per seat but procurement scale: it is scoped for hospital groups, public health systems and teleradiology providers. Weigh it against point-tool vendors such as Aidoc or Viz.ai on cost per study across all three modalities rather than on sticker price.
In short
Harrison.ai — Clinical imaging AI that gives radiologists an assistive second read on chest X-ray, CT brain, and CT chest studies. Best for High-volume hospital radiology departments reading chest X-ray and head CT at scale, Public health systems and large private hospital groups standardising imaging AI across sites, Teleradiology providers that need consistent finding coverage across many referring sites. Contact Sales pricing.
Viability Score
How well maintained and how widely used is Harrison.ai? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Chest X-ray decision support detecting up to 124 findings in under 20 seconds
- CT Brain decision support on non-contrast head CT detecting up to 130 findings in under 90 seconds
- CT Chest decision support detecting up to 167 radiological features
- Harrison.Rad 1.5 radiology-specific foundation model, announced January 2026 for public research use
- Worklist prioritisation flagging patients for earlier diagnostic and treatment pathways
- Assistive second-reader output designed to fit existing radiologist reporting habits
- Named use cases across emergency and acute care, oncology, incidental detection, and chronic disease
- Deployment models for public hospitals, private hospitals, private radiology providers, and teleradiology groups
- Clearance for clinical use in 40+ countries, with region-specific feature availability
- Clinical evidence and case study library published on the vendor site
- Clinician adoption reported at 3,500 users across 1,000+ sites and four continents
- Harrison.ai Comprehensive Care programme combining the imaging tools across the clinical workflow
- Reduction of chaotic worklists by surfacing urgent findings inside non-urgent queues
- Faster report turnaround through AI-assisted case throughput
About Harrison.ai
Harrison.ai sells clinical decision-support AI for medical imaging that slots into the radiology reporting workflow rather than running beside it. A radiologist opens a study and the tool has already flagged candidate findings, so the AI behaves as an assistive second reader layered onto existing reporting habits — not a replacement reporting queue. The product line spans three modalities. Harrison CXR detects up to 124 findings in under 20 seconds; CT Brain detects up to 130 findings in under 90 seconds on non-contrast head CT; CT Chest surfaces up to 167 radiological features. All three feed a worklist prioritisation layer that flags patients for earlier diagnostic and treatment pathways, which is where the operational case is made: turnaround time on urgent findings matters as much as diagnostic accuracy. Underneath sits Harrison.Rad 1.5, the company's radiology-specific foundation model, which the vendor announced in January 2026 as publicly available for research use — a research status, not a productised clinical feature. Scale claims here are unusually concrete for imaging AI. The vendor reports 3,500 clinicians at more than 1,000 sites across four continents, more than 35% of all chest X-rays in NHS England processed with Harrison CXR, every public A&E department in Hong Kong using the tools, 130-plus hospitals across 45-plus NHS Trusts in the UK, clearance for clinical use in 40-plus countries, and $240M in funding. Deployments are named for public hospitals, private hospitals, private radiology providers, and teleradiology groups, spanning emergency and acute care, oncology management, incidental detection, and chronic disease management. Against Aidoc and Viz.ai, Harrison.ai competes on breadth of findings per study across three modalities rather than single-exam triage or care-coordination handoffs. It is a health-system-scale purchase, and the site is written for healthcare professionals only — the products are explicitly not sold to the general public.
Behind the Verdict
The pitch here is breadth under one contract. Most imaging AI vendors sell you a detector for one exam type and then a second product for the next one; Harrison.ai bundles chest X-ray (up to 124 findings in under 20 seconds), non-contrast head CT (up to 130 findings in under 90 seconds) and chest CT (up to 167 radiological features) behind a single worklist prioritisation layer. For a hospital group or teleradiology provider running many sites, that consolidation is the real product — one integration to your reporting workflow, one clinical governance package, one vendor relationship. The adoption evidence is the strongest part of the story. More than 35% of all chest X-rays in NHS England processed with Harrison CXR, every public A&E department in Hong Kong, 130-plus hospitals across 45-plus NHS Trusts in the UK, 3,500 clinicians at 1,000-plus sites on four continents, and clearance in 40-plus countries. Those are production numbers rather than pilot logos, and they matter more than a benchmark table when you are preparing a business case for a trust board or a hospital executive committee. The assistive framing is deliberate and worth respecting: the AI flags candidate findings and the radiologist still reads and signs the study. That keeps you inside existing reporting habits, but it also means the value case lives in worklist prioritisation and report turnaround time rather than in headcount reduction. If your bottleneck is downstream care coordination — alerts paging a stroke team, handoffs into a treatment pathway — Viz.ai and Aidoc are built more directly around that motion. Two caveats to put in front of your procurement team. First, the vendor's own pages warn that product and features may not be approved or available in your region, so the finding set you can contract for depends on where you operate; scope it country by country. Second, Harrison.Rad 1.5, announced in January 2026, is publicly available for research use — a different status from a cleared clinical feature, so do not let it drift into the clinical scope of a tender. Public materials describe clinical benefit and deployment scale but not onboarding timelines, integration specifics, or which PACS/RIS interfaces are certified. Budget your own IT and integration effort rather than assuming a fixed timeline, and treat the case study library as a reference list to call, not as an implementation plan.
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Real-world workflow fit
Concrete scenarios for the personas Harrison.ai actually fits — and what changes day-one when you adopt it.
Chest X-rays from A&E and inpatient wards stack up overnight and urgent findings sit behind routine outpatient films. The department turns on Harrison CXR on the existing reporting workflow so candidate findings and prioritisation flags are waiting when the morning read starts.
Outcome: Urgent findings surface at the top of the worklist, and the radiologist reads and signs as before — the second-read layer changes ordering, not the reporting habit.
Referrers across dozens of sites send a mix of chest X-ray and non-contrast head CT. Standardising on one detection layer means every reporting radiologist, wherever they sit, sees the same finding coverage on the same study types.
Outcome: One vendor contract and one detection standard across referring sites, with head CT findings surfaced in under 90 seconds.
Chest CT follow-up volume is rising and incidental findings get missed between scheduled scans. CT Chest detection surfaces up to 167 radiological features so incidental findings enter a review pathway instead of sitting in the queue.
Outcome: Incidental and follow-up findings become visible earlier in the worklist, feeding earlier diagnostic and treatment pathways.
Use Cases
- Flag chest X-ray findings for prioritised reporting so subtle and complex cases get a second set of eyes.
- Surface non-contrast head CT findings in under 90 seconds to route urgent cases faster.
- Detect up to 167 radiological features on chest CT to support oncology and chronic disease follow-up.
- Prioritise worklists so flagged patients reach diagnostic and treatment pathways sooner.
- Support teleradiology reading across distributed sites with a consistent detection layer.
- Standardise detection coverage across multiple hospital sites under one vendor contract.
Models Under the Hood
as of 2026-09-14
Limitations
- Public materials describe clinical benefit and deployment scale but not onboarding timelines or integration specifics, so budget integration effort with your own IT team.
- Product pages carry the caveat that not all features are available in all regions, which means the finding set you can contract for depends on where you operate.
- Algorithm outputs are assistive — radiologists still read and sign the study.
- Deployment is health-system scale: named references are public hospitals, private hospital groups, and large radiology providers, and the site's own use-case framing assumes that scale.
- Harrison.Rad 1.5 is announced as available for research use, a different status from a productised clinical feature.
- The site is addressed to healthcare professionals and states the products are not available for purchase by the general public.
as of 2026-10-19
Verification history
We have re-verified Harrison.ai 21 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 21 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Harrison.ai's pricing actually pencils out — and where peers do it cheaper.
Harrison.ai sells to healthcare organisations and does not publish a tier list, so the comparison that matters isn't price per seat but procurement scale: it is scoped for hospital groups, public health systems and teleradiology providers. Weigh it against point-tool vendors such as Aidoc or Viz.ai on cost per study across all three modalities rather than on sticker price.
Setup time & first value
How long it actually takes to get something useful out of Harrison.ai — broken out by persona, not the marketing-page minute.
Expect a health-system deployment rather than a same-day switch-on: procurement, clinical governance review and PACS/RIS integration all sit on your side. Chest X-ray coverage is the usual first modality to go live, with head CT and chest CT layered on after the first integration is stable. The vendor publishes case studies rather than timelines, so plan your own integration estimate.
Switching to or from Harrison.ai
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual radiologist double-reading: layer Harrison CXR onto your existing reporting workflow so flagged findings appear alongside the study rather than in a parallel queue.
- →From a single-modality triage tool: consolidate chest X-ray, head CT and chest CT detection under one contract and one imaging IT integration.
- →From a site-by-site pilot: use the worklist prioritisation layer to standardise finding coverage across every hospital in the group.
- →From an in-house research model: Harrison.Rad 1.5 is available for research use, so academic groups can evaluate the foundation model before any clinical procurement.
- ↗To Aidoc: if your bottleneck shifts to care-coordination handoffs and clinical alerting rather than reporting support.
- ↗To Viz.ai: if the priority becomes downstream treatment-pathway coordination for time-critical conditions.
- ↗To single-modality point tools: if chest X-ray volume dominates and you no longer need head CT or chest CT breadth under one vendor.
Resources & Guides
Tutorials & Learning
Harrison.ai plans to dominate healthcare AI. How?
The AI Guide
harrison.ai -Engineering
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