Diagnostics & imaging AI · Dental X-ray and CBCT AI
Pearl
Pearl is a California dental AI company whose Second Opinion software, the first FDA-cleared AI to read dental X-rays, outlines disease on 2D X-rays and 3D scans, with practice, insurance and voice tools.
Strong fit
- Dental practices that want FDA-cleared AI for both 2D X-rays and 3D CBCT scans
- Groups that want AI findings, insurance checks and production reports from one vendor
- Practices outside the US, since Pearl holds clearances in many countries
Weak fit
- Medical imaging departments
- Practices that want imaging software and AI from a single install
- Buyers who need a posted price
Bottom line
Pearl is a strong choice for dental practices and groups that want widely used X-ray AI that also reads 3D scans, with front desk and claim tools alongside. Its reach outside the US suits international groups. Buyers should price the modules they need, since Pearl does not post a price.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Ophir Tanz, Cambron Carter and Dr. Kyle Stanley founded Pearl in 2019, spinning it out of GumGum, the computer vision company Tanz had co-founded. Tanz is chief executive, Carter is chief technology officer and Stanley, a dentist, is chief clinical officer. Pearl is based in West Hollywood, California, and has raised $69 million.
Pearl's Second Opinion reads dental X-rays in real time and outlines decay, calculus, root abscesses and other findings for the dentist to confirm. In March 2022 it became the first AI software cleared by the FDA to read dental X-rays. Pearl now holds clearances for both 2D X-rays and 3D CBCT scans, and says it has approvals in about 50 countries.
Around detection, Pearl sells Practice Intelligence, which scans past X-rays for care that was missed, plus insurance verification, claim preparation, clinical training for dental schools and ambient voice notes for exams and perio charting. Its AI is built into Henry Schein One's Dentrix Ascend.
Pearl says more than 29,000 practices have chosen it and that dentists using it see about 30% more patients accept treatment. In its FDA studies, readers using the AI found 36% more lesions than readers without it.
Pearl does not post prices. Practices should check that their imaging and practice management systems are supported before signing, and confirm which clearances apply where they work.
Overjet fits dental groups that want X-ray AI tied to imaging software, voice charting and payer claim review. Videa fits dental groups that want detection, voice notes and claims tools from one vendor.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Overjet | 8.4 | 8.2 |
| Pearl | 8.1 | 7.9 |
| Videa | 7.8 | 8.1 |
Pricing
| Item | Detail |
|---|---|
| Model | Pearl sells its dental AI by subscription, with modules for diagnosis, practice analytics, insurance and 3D imaging. |
| What usually drives cost | For Pearl, cost depends on the number of locations and on which modules you add, such as Second Opinion 3D, insurance verification or ambient voice notes. |
| What to ask before you buy | Ask Pearl for a price per location for each module, which imaging and practice management systems it supports in your offices, and whether its clearances cover your country. |
| Published pricing | Pearl does not publish a price list. |
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.