Score the best dental AI software against three tests taken in order, and treat a failure at any one test as disqualifying no matter how the platform performs on the rest. Test one, accuracy: FDA clearance for the indications you will use chairside, and clinical validation published outside the vendor's own materials. Test two, integration: the platform loads inside the practice management system your team already opens, providers keep using it daily, and the security terms sit in a signed contract. Test three, ROI: the vendor names the number it will move and commits to a figure.
A platform can hold real FDA clearance and still lack clearance for the work you bought it to do. Vendors make integration and ROI claims that a demo supports and a contract does not, and taking the tests in order catches that before you sign.
A 2025 umbrella review with meta-analysis in PLOS One pooled 20 studies covering 29,423 diagnostic tests and reported a pooled sensitivity of 85% and a specificity of 90% for AI detection of dental caries (PLOS One, 2025). Ask every vendor to point you to results at that level, and treat accuracy claims with no published study behind them as unverified.
For the printable scorecard alone, start with how to evaluate a dental AI platform. This guide covers how to weigh the six criteria against each other, what disqualifies a vendor outright, and the evidence behind each check. For earlier background on the wider software stack, see our AI buyer's guide to dental practice software.
What disqualifies a platform on the spot
Any one of these ends the evaluation before you score the rest.
No FDA clearance for the indication you plan to use chairside. Verify every K-number on fda.gov yourself.
No published, peer-reviewed validation. Internal accuracy numbers with no study behind them stay unverified.
No native integration with a system one of your locations runs, and no dated roadmap for it.
No utilization reporting by site. A vendor that cannot show which offices use the platform cannot tell you when adoption slips.
Any hesitation on the security terms: end-to-end encryption, de-identified imagery, a signed Business Associate Agreement, and written confirmation that the vendor will not train its models on your patient data.
No number the vendor will commit to moving. Ask for case acceptance, production per visit, or denial reduction by name.
Test one: FDA clearance and published validation
A dental AI platform analyzes radiographs to detect and measure conditions, and the dentist makes the diagnosis. Unreliable findings cost a practice more than using no software at all, so confirm two things before you look at any other feature: the cleared indication and the published evidence behind it.
Match the clearance to the indication you will use chairside
Clearance attaches to an indication, not to a company. Caries detection clearance does not carry over to bone level measurement, so ask which indications a vendor holds, get every K-number in writing, and look each one up on fda.gov yourself. The clearance letter names the exact indication in a way a sales deck rarely does.
Overjet is FDA-cleared to detect caries and measure bone levels on bitewing and periapical radiographs. Anything a vendor cannot show a clearance letter for is unproven, whatever the demo displays.
How to read a validation study
Read past the headline number. Ask for the study itself, the sample size, the journal, and what the AI was measured against, since a model compared to a single reviewer is a weaker result than one compared to a consensus panel or to histology.
Look for external validation on data the model never saw in training, and for a study population that resembles your patients in age, caries risk, and imaging equipment. A high score on an in-house test set tells you less than a result independent researchers reproduced.
Test two: integration your providers actually use
A platform that clears test one still produces nothing until it runs inside the workflow your team already has. Test two covers three checks: native integration, adoption support, and security.
Native integration with your practice management system
Your providers open the chart for every patient. If the AI findings do not appear there, they will not appear in the exam either, so confirm native integration with every practice management system your practice or group runs, including Dentrix, Eaglesoft, and Open Dental.
Ask to see the findings load inside that software during the demo, on a radiograph, rather than in a second application your team has to switch to. If your locations run different systems, ask which ones are native today and which sit on a roadmap, then get the roadmap dates in writing.
Provider adoption and utilization support
Ask the vendor how it measures daily utilization, and ask for the figure by location rather than as a group average, since an aggregate hides the two offices that stopped opening the tool in month three. A committed partner tracks usage by site and steps in with training when a location falls behind.
Ask for the implementation timeline and what onboarding looks like across multiple sites, because a rollout that stalls at go-live delays every dollar of return. Handing your team a login and walking away leaves adoption to chance, and low adoption is the most common reason a platform never returns its cost.
Security, HIPAA, and a signed BAA
Settle the security terms in the contract before the pilot starts. The list is short: end-to-end encryption, de-identified imagery, a signed Business Associate Agreement, and contract language barring the vendor from training its models on your patient data.
Ask who at the vendor signs the BAA and how long their legal review runs, because a redline cycle that stretches three weeks pushes your go-live and every dollar behind it. Treat hesitation on any item as a disqualifier.
Test three: a number the platform commits to moving
Three numbers carry most of the return: case acceptance, production per visit, and denial reduction. Ask every vendor to name the metric it expects to move and the size of the lift, then run the math before the demo. Your number of locations times the expected lift times your average case value gives you the annual figure the contract has to beat.
Case acceptance moves when the patient sees the same color-coded finding on the screen that the dentist sees. Overjet reports that practices using its AI have seen a 10% to 20% increase in case acceptance. Ask each vendor for its own range and for what it measured to produce that range, then apply the low end to your case volume rather than the high end.
Production per visit rises when the AI surfaces existing conditions a rushed exam passes over. The dentist diagnoses and plans more of each patient's needed treatment in chair time you already pay for. Ask a vendor to show the change in diagnosed treatment per visit at practices similar to yours, not just a headline accuracy figure.
Objective radiographic evidence attached to a claim gives payers the documentation they need to approve clinically necessary treatment the first time, which protects revenue your team has already earned.
Lisa Khandakji, Office Manager at DANV Potomac, resubmitted denials with Overjet radiographs over a two-week period and had 90% of them overturned. Ask a vendor whether it can show that kind of documentation outcome for practices like yours.
Overjet reports detection improvements ranging from 15% to 43% across its studies, meaning providers using the platform find pathology they would otherwise pass over. Providers who catch more, show it clearly, and document it properly convert more of those findings into accepted, reimbursed treatment.
Apply the buyer's guide framework
Book a demo to see how Overjet Vision AI performs on FDA clearance, published validation, native integration, adoption support, and measurable ROI.
Dental AI buyer's guide FAQ
What makes the best dental AI software?
A platform that clears three tests in order: accuracy, integration, and ROI. It holds FDA clearance for the indication the practice will use and published clinical validation behind it, it runs inside the practice management system the team already opens, and it commits to moving a specific number. A failure at any one test disqualifies the platform regardless of how it performs on the others.
Is AI caries detection supported by published research?
Yes. A 2025 umbrella review with meta-analysis in PLOS One pooled 20 studies covering 29,423 diagnostic tests and reported a pooled sensitivity of 85% and a specificity of 90% for AI detection of dental caries.
Does dental AI diagnose patients?
No. Dental AI detects and measures conditions on the radiograph and marks them for review. The dentist reviews those findings and makes the diagnosis.
How do you measure ROI on dental AI?
Tie the platform to a metric you already track, such as case acceptance, production per visit, or denial reduction. Size the return as your number of locations multiplied by the expected lift multiplied by your average case value.
Put a platform through all three gates
Book a demo and run Overjet Vision AI through the gates in order. Ask for the K-numbers, the published study behind the accuracy claims, a live look at the findings loading inside your practice management system, utilization reporting by location, and the number Overjet expects to move for a practice your size.
