Diagnostics & imaging AI · Chest X-ray and CT brain AI
Harrison.ai
Harrison.ai makes Annalise, AI software that reviews chest X-rays and head CT scans and flags a wide range of findings for radiologists, and Franklin.ai, AI for pathology slides.
Strong fit
- Radiology groups and hospitals that want AI to flag many findings on chest X-rays and head CT scans
- Emergency departments that want urgent findings on X-rays and head CTs seen sooner
- Teleradiology providers that read high volumes for hospitals short of radiologists
Weak fit
- Buyers who want breast screening or cardiac imaging AI
- Organizations that want AI to read and report exams with no radiologist
- Buyers who need a published price list
Bottom line
Harrison.ai is worth using for radiology teams that want one AI tool to catch a wide range of findings on chest X-rays and head CT scans. It suits busy departments that want fewer missed findings. Buyers focused only on fractures or lung nodules may prefer AZmed or Riverain.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Harrison.ai, based in Sydney, Australia, makes Annalise, AI software that reviews chest X-rays and head CT scans. It also makes Franklin.ai, AI for pathology slides.
Brothers Aengus Tran, a doctor trained at the University of New South Wales, and Dimitry Tran founded it in 2018 after growing up in Vietnam and moving to Australia.
Annalise reviews a chest X-ray or a head CT scan and lists findings for the radiologist, so fewer problems are missed on busy days. In some markets it is cleared for up to 124 findings on chest X-ray and 130 on CT brain.
Franklin.ai reads tissue slides, starting with prostate biopsies. Its second foundation model, Harrison.rad.1.5, drafts radiology reports for X-rays.
Those products are in wide use. Hospitals and radiology groups in more than 25 countries use Harrison.ai, and it holds 12 FDA clearances.
It raised a $112 million Series C in February 2025 and says more than 40% of its business comes from the United States.
In 2026 it cut about 15% of its staff to focus on sales. It supplies the technology for Frontier Radiology, a U.S. teleradiology practice it does not own.
Other tools fit narrower needs. Qure.ai suits chest and emergency imaging AI across many countries. Riverain Technologies suits a focus on lung nodules on chest images.
AZmed suits fracture and X-ray detection, and Aidoc suits a health system that wants AI across many imaging types.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Aidoc | 7.9 | 7.5 |
| ScreenPoint Medical | 7.9 | 7.6 |
| Harrison.ai | 7.7 | 7.5 |
| Qure.ai | 7.4 | 7.1 |
| Riverain Technologies | 7.3 | 6.9 |
| Subtle Medical | 7.2 | 6.8 |
| DeepTek | 7.1 | 6.8 |
| AZmed | 6.9 | 6.7 |
| Enlitic | 6.9 | 6.5 |
| Avicenna.AI | 6.9 | 6.6 |
| Ferrum Health | 6.8 | 6.5 |
| Koios Medical | 6.7 | 6.3 |
Pricing
| Item | Detail |
|---|---|
| Model | Software subscription quoted by Harrison.ai. |
| What usually drives cost | How many exams you read, which products you use (chest X-ray, CT brain, or pathology), the number of sites, and connections to your image and reporting systems. |
| What to ask before you buy | The price per exam or per site, which FDA-cleared findings are included in the U.S., and the cost to connect it to your systems. |
| Published pricing | Harrison.ai does not publish a price list. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Harrison.ai to function | |
| Lead radiologist for AI | Decides how findings are shown. |
| A connection to your image and reporting systems | Findings appear where reading happens. |
| Starting reading times and miss rates | This shows change later. |
| Policy on AI findings | Radiologists need clear rules. |
| IT support for integration | Studies must route to the AI. |
| What will maximize your value | |
| Start with chest X-rays | Highest volume and widest coverage. |
| Move urgent findings to the top of reading lists | Gets critical cases read first. |
| Audit flagged findings each quarter | This shows where AI helps and where it flags too much. |
| Deal-breakers | |
| You need breast or heart imaging AI | |
| You expect AI to report with no radiologist | |
| No radiologist owns it | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract and integration | 1-3 months |
| 2 | Chest X-ray live | 2-4 months |
| 3 | CT brain and more sites | 4-9 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.