Diagnostics & imaging AI · Chest and emergency radiology AI
Qure.ai
Qure.ai provides radiology AI algorithms that help hospitals prioritize critical findings on chest X-ray, CT, and related studies inside PACS worklists. It does not replace the radiologist.
Strong fit
- Hospitals and imaging networks that need AI triage for chest X-ray, CT, and emergency findings
- Buyers comparing mid-market radiology AI vendors with global deployments rather than US-only point tools
- Groups that want worklist prioritization and decision support without replacing the radiologist
Weak fit
- Clinics with no imaging volume and no PACS integration path
- Buyers that only need point-of-care ultrasound AI for a single device fleet
- Organizations that refuse any third-party algorithm on diagnostic images
Bottom line
Qure.ai earns a Recommend on our diagnostics and imaging AI board for health systems that want chest and emergency radiology triage at mid-market cost. The Mumbai-founded company sells FDA-cleared and globally deployed algorithms that prioritize critical findings on worklists rather than replacing radiologists. Public filings and third-party coverage put recent annual revenue near the low-$20M band with a few hundred employees, inside the prefer band. Implementation needs PACS/RIS integration and clinical validation. Pricing is per-study or enterprise license. Score sits below Aidoc on US acute orchestration breadth and above smaller single-anatomy point tools when multi-finding chest and neuro triage is the job.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Qure.ai is a Mumbai-founded radiology AI company led by co-founder and CEO Prashant Warier. Hospitals use its algorithms to flag and prioritize critical findings on chest X-rays, CTs, and related studies inside existing PACS worklists.
It is imaging AI decision support, not a full PACS replacement and not an ambient documentation tool. Compare Aidoc when US acute care orchestration is the buy, Riverain when chest X-ray detection depth is the focus, and AISAP when the problem is point-of-care ultrasound AI rather than radiology worklist triage.
Score reflects broad multi-finding deployments and regulatory clearances, with implementation effort typical of any PACS-integrated AI.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Aidoc | 7.9 | 7.5 |
| Qure.ai | 7.4 | 7.1 |
| 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 |
| Ferrum Health | 6.8 | 6.5 |
| Koios Medical | 6.7 | 6.3 |
Pricing
| Item | Detail |
|---|---|
| Model | Per-study or enterprise radiology AI licenses; quote-based by modality and volume. |
| What usually drives cost | Study volume by modality, algorithm pack, deployment regions, and support. |
| What to ask in diligence | Cost per study or annual license at your chest/CT volume, including PACS integration. |
| Published pricing | No public US list price; enterprise and regional quotes. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Qure.ai to function | |
| PACS/RIS integration owner | Algorithms need a worklist path. |
| Radiology champion named | Alerts fail without clinical ownership. |
| Validation cohort planned | Skip validation and trust collapses. |
| IT security review for image AI | DICOM AI stalls without approval. |
| Escalation rules for critical findings | Prioritization without process creates liability. |
| What will maximize your value | |
| Pilot one modality first | Multi-modality day one multiplies tickets. |
| Measure turnaround on critical studies | Prove triage value. |
| Align with existing AI governance | Duplicate AI tools confuse radiologists. |
| Deal-breakers | |
| No imaging volume | |
| Refuse third-party image AI | |
| No PACS access | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Integration and validation | 6-12 weeks |
| 2 | Pilot modality | 4-8 weeks |
| 3 | Expansion | 8-16 weeks |
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.