Diagnostics & imaging AI · Imaging data standardization
Enlitic
Enlitic provides AI-assisted medical imaging data standardization and related analytics for radiology archives. It is imaging-data infrastructure, not a pure ED triage detector.
Strong fit
- Imaging departments that need AI help to clean up and move radiology data between archives
- Health systems fixing messy labels on old imaging studies before analytics or AI projects
- Buyers comparing imaging data tools, not only tools that detect urgent findings
Weak fit
- Radiology groups that only need a tool to flag stroke or blood clots in the lungs, with no data to move
- Clinics with no imaging archive problem
- Buyers who need proof from many U.S. hospitals using detection AI
Bottom line
Enlitic is worth considering for imaging departments whose main job is cleaning up, standardizing, and migrating imaging data. It suits buyers who need better data quality across archives. Emergency departments that only need fast detection of urgent findings should look at Aidoc or Avicenna.AI.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Enlitic is an imaging AI and data company listed on the Australian Securities Exchange as ENL and led by CEO Michael Sistenich.
It focuses on cleaning up and analyzing medical imaging data across archives, not on detecting a single type of finding. It is a tool for imaging data, not for emergency triage.
That focus makes it useful when data quality and migration are the job. Its commercial scale is still early compared with larger detection companies such as Aidoc.
Among imaging AI tools we review, Enlitic scores close to Avicenna.AI, above Koios, and below Riverain and DeepTek, which are more widely used for detection.
Aidoc or Avicenna.AI fit buyers who want real-time triage of findings. Riverain fits chest X-ray and CT detection, and DeepTek fits buyers who need a broader radiology AI platform.
Enlitic is still small, even though the data cleanup job is real.
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 | A software license for cleaning up imaging data, plus related modules. Quotes depend on your sites and archives. |
| What usually drives cost | How much data is in your archives, which modules you buy, services, and support. |
| What to ask before you buy | The full first-year cost of the license and services for your archive size and the systems you are moving to. |
| Published pricing | Enlitic does not publish a simple price list or per-user prices. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Enlitic to function | |
| A named radiology IT owner for connecting the archive and AI | Imaging tools with no owner stall in the image system queue. |
| A list of your image archives and their labeling problems | Unknown archive problems block the move. |
| A starting count of broken studies, mislabels, or rework | You cannot prove value without a before number. |
| An agreed time window for changes with radiology operations | Surprise switches disrupt reading. |
| A plan for clinicians to check any AI results | Unreviewed automation creates safety risk. |
| What will maximize your value | |
| Measure rework and labeling errors every month | Vendor demos are not results. |
| Test one imaging type or site before announcing it everywhere | Narrow wins beat empty rollouts. |
| Keep getting radiologist feedback during the test | Silent frustration kills adoption. |
| Write down how to undo changes to the archive | Failed migrations need a safe exit. |
| Send a monthly imaging operations summary | Hidden friction surprises everyone. |
| Deal-breakers | |
| No one will own connecting the image archives. | |
| You will not have clinicians check AI results. | |
| You expect a data cleanup tool to flag strokes in the emergency department. | |
| Archive vendors block the connections it needs. | |
| Leaders will not measure rework or errors. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Plan and measure the starting point | 2-4 weeks. List archives, set measures, and define success. |
| 2 | Set up | 6-16 weeks. Connect systems, map data, and check results. |
| 3 | Pilot | 1-2 months. One site or imaging type, with a guide for problems. |
| 4 | Expand | 2-6 months. Add archives and stop manual fixes. |
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.