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 service lines that need AI-assisted standardization and migration of radiology data across archives
- Health systems cleaning messy historical imaging metadata before analytics or AI overlay projects
- Buyers comparing mid-market imaging-data peers rather than only detection triage vendors
Weak fit
- Radiology groups that only need a stroke or PE triage algorithm with no data-migration scope
- Clinics without an imaging archive problem
- Buyers that require a large U.S. detection-AI installed base as the primary proof point
Bottom line
Enlitic earns a Conditional on our diagnostics and imaging AI board. The ASX-listed company led by CEO Michael Sistenich focuses on imaging data standardization and related analytics rather than a single triage finding. FY2025 revenue near $3.8M keeps it inside the prefer band but also signals earlier commercial scale than Aidoc-class peers. Buy when data quality and migration are the job; look elsewhere when ED triage detection is the only need. Score sits with Avicenna.AI and above Koios on this board, below Riverain and DeepTek on mainstream detection traction.
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 (ASX:ENL) and led by CEO Michael Sistenich. The product focus is standardizing and analyzing medical imaging data across archives.
It is imaging-data infrastructure, not a pure ED triage detector. Compare Aidoc or Avicenna.AI when real-time finding triage is the buy, Riverain when chest X-ray/CT detection is the lane, and DeepTek when a broader radiology AI platform is required.
Score is Conditional because commercial scale is still early versus detection leaders, even though the data-standardization job is real.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Aidoc | 7.9 | 7.5 |
| Riverain Technologies | 7.3 | 6.9 |
| Studycast | 7.2 | 6.9 |
| Subtle Medical | 7.2 | 6.8 |
| DeepTek | 7.1 | 6.8 |
| AISAP | 7.0 | 6.6 |
| Enlitic | 6.9 | 6.5 |
| Avicenna.AI | 6.9 | 6.6 |
| Koios Medical | 6.7 | 6.3 |
Pricing
| Item | Detail |
|---|---|
| Model | Software licensing for imaging data standardization and related modules; site and archive scope drive quotes. |
| What usually drives cost | Archive volume, modules, professional services, and support. |
| What to ask in diligence | All-in first-year license plus services for your archive size and target systems. |
| Published pricing | Public list price: not a simple menu; listed company disclosures give revenue context but not seat prices. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Enlitic to function | |
| Named radiology IT owner for archive/AI integration | Imaging tools without owners stall in PACS queues. |
| Inventory of PACS/VNA and metadata quality issues | Unknown archive debt blocks migration. |
| Baseline of broken studies, mislabels, or rework rates | You cannot prove value without a before number. |
| Change window with radiology operations agreed | Surprise cutovers disrupt reading. |
| Clinical validation plan for any AI outputs in scope | Unreviewed automation creates safety risk. |
| What will maximize your value | |
| Measure rework and metadata error rates monthly | Vendor demos are not outcomes. |
| Pilot one modality or site before enterprise banners | Narrow wins beat empty rollouts. |
| Keep radiologist feedback loops during pilot | Silent frustration kills adoption. |
| Document rollback for archive writes | Failed migrations need a safe exit. |
| Publish a monthly imaging-ops digest | Hidden friction surprises everyone. |
| Deal-breakers | |
| Nobody will own PACS/VNA integration. | |
| You refuse validation for AI-assisted outputs. | |
| You expect ED stroke triage from a data-standardization tool alone. | |
| Archive vendors block the required interfaces. | |
| Leadership will not measure rework or error rates. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Scope & baseline | 2-4 weeks - Archive inventory, metrics, success tests. |
| 2 | Configure | 6-16 weeks - Integrations, mapping, validation. |
| 3 | Pilot | 1-2 months - One site/modality; issue playbook. |
| 4 | Scale | 2-6 months - Expand archives; retire manual fixes. |
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 actually 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.