Diagnostics & imaging AI · Radiology AI suite
Aidoc
Aidoc is a radiology AI suite for hospitals that want always-on triage and notification on imaging studies. It flags suspected acute findings and routes alerts into radiology and care-team workflows. It is not a full PACS replacement.
Strong fit
- Health systems that want AI checking every scan for urgent findings around the clock
- Radiology leaders who track how fast urgent findings reach the care team and how many are missed
- Organizations with IT time to connect it to PACS and the EHR, and a clear plan for managing alerts
Weak fit
- Sites with no radiology IT owner to manage where AI alerts go
- Buyers who want one narrow detection algorithm, not a platform
- Groups unwilling to pay for changing how radiologists work
Bottom line
Aidoc is worth using for health systems that want AI to flag urgent findings on scans and get them to the right clinicians faster. It suits radiology teams with IT support for PACS and a clear plan to keep alerts from piling up. Buyers who only want one narrow detection algorithm should look elsewhere.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Aidoc makes imaging AI that flags urgent findings on scans and alerts the right clinicians. Its main selling point is getting critical findings to the care team faster.
Aidoc has many FDA-cleared algorithms, but they help only if radiologists can act on the alerts. When the system flags too many findings that do not matter, teams start ignoring the list, and that is how these tools fail.
Setting it up takes real work connecting to PACS and agreeing on rules for alerts. Pricing is set for each organization, often per study or as a platform license.
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 |
| Optellum | 7.4 | 7.0 |
| 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 | Often a platform fee plus a charge per study or per package of algorithms. |
| What usually drives cost | Connecting to PACS and training radiologists, on top of the license. |
| What to ask before you buy | A pilot on your three most important urgent conditions before adding more algorithms. |
| Published pricing | Aidoc does not publish prices. Expect a custom quote, and confirm the total cost at your volume. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Aidoc to function | |
| IT time to connect to PACS and the EHR, and a radiology IT owner | If no one manages where alerts go, they become noise on the reading list. |
| A clear plan, agreed with radiologists, for managing alerts | Too many alerts that do not matter teach people to ignore the list. |
| Specific urgent conditions to watch for, each with a clinical champion | A broad platform helps only if you have defined what happens after each alert. |
| A budget for changing how radiologists work | Buying algorithms without changing the workflow stalls after the pilot. |
| Willingness to start with a short list of the most useful algorithms | Turning everything on at once often destroys trust in the tool. |
| What will maximize your value | |
| Track how fast urgent findings reach the team and how many are missed | Those are the results Aidoc is built to improve. |
| Pilot your most important urgent conditions before adding algorithms | Build trust on a few findings first. |
| Have radiology and the emergency department (or other specialties) manage alert overload together | Coordination fails when only one department deals with too many alerts. |
| Fit alerts into the reading list radiologists use today | Extra screens get ignored. |
| Work out per-study or platform fees at your volume before you sign | Teams that only heard about pilot pricing are often surprised by the full cost. |
| Deal-breakers | |
| No one in radiology IT will manage where AI alerts go. | |
| You only want one narrow detection algorithm, with no platform or plan for managing alerts. | |
| Radiologists and leadership will not pay for changing how radiologists work. | |
| Managing alerts is ruled out of the project. | |
| Connecting to PACS cannot happen in the next two quarters. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | From signing to kickoff | 3-8 weeks for security review, PACS access, and choosing which conditions to cover first |
| 2 | From kickoff to first use | 10-18 weeks for the first urgent-finding algorithms to appear in radiologists' reading lists |
| 3 | From first use to steady results | 3-7 months of adjusting alert settings and adding more conditions |
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.