Buying Dental AI: An Evidence and Workflow Checklist for UK Practices
Research and guidance checked: 6 October 2026.
Cover photograph: Dental clinic equipment and a monitor displaying dental images. Illustrative stock photo; not an AI output or a research case. Photo: Polina Zimmerman / Pexels • Pexels licence
Buying dental AI is easier when the practice starts with a problem statement. 'We want AI' leaves the demonstration in charge of the decision. 'We want to improve consistency in this specific workflow, within this review process' gives the team criteria it can test.
The 30-second answer
Define the task and success measures before evaluating products.
Ask for validation that matches the product version and intended setting.
Measure the complete workflow, including corrections, failures and costs.
Ask for a claim you can test
Write down the precise task, input and output. Is the product highlighting a radiographic feature, tracing anatomy, drafting notes or recommending an option? Ask what it is not designed to do. Clarifying the intended purpose is central to assessing software medical-device information.
Use evidence questions that expose the boundaries
Supplier question | Why the answer matters |
|---|---|
Which version was studied? | Updates can change the relevant performance |
Was the test set independent? | Training overlap can distort confidence |
Who and what were represented? | Patient and imaging differences affect relevance |
What reference standard was used? | It defines what counted as correct |
What were the false-positive and false-negative results? | A single accuracy score hides different errors |
Was clinician-assisted performance measured? | A model score is not the whole workflow |
Ask for the paper or report behind the claim, not just a slide containing the percentage. Note whether the unit was a surface, tooth, image or patient. Check whether the validation included difficult inputs and whether excluded cases resemble everyday work.
Research maturity deserves its own question
The GDC's 2025 real-world dental AI assessment included 45 studies and identified gaps in implementation evidence. Use that context to ask suppliers for direct evidence about their workflow rather than relying on general enthusiasm about the research field.
Healthcare colleagues reviewing information together on a laptop. Illustrative stock photo; not an AI output or a research case. Photo: Nadezhda Moryak / Pexels • Pexels licence
Build a pilot with a written decision rule
A proposed pilot can compare the current process with the assisted process on appropriately authorised material. Record quality, errors, time to reviewed output and technical failures. Define who reviews results and what would make the practice adopt, extend, modify or stop the pilot. This is an original evaluation proposal, not a claim that a short pilot can establish clinical safety.
Area | Example measure |
|---|---|
Clinical output | Reviewed disagreements and material corrections |
Workload | Time to final usable output |
Technical reliability | Failed imports, unavailable service and export problems |
Patient communication | Whether the team can explain the actual function |
Governance | Unresolved data or device-purpose questions |
Cost | Recurring cost plus implementation and review work |
Calculate a realistic total cost
Include subscription tiers, setup, integrations, training, additional storage and any per-use fees. Add the staff time required to review and correct outputs. Ask how pricing changes with users or volume, what happens at cancellation and how records or reviewed outputs can be exported. A low entry price can conceal a difficult exit.
Make the governance questions concrete
Obtain a clear description of data flows and retention.
Resolve the practice's approval process for the proposed use.
Check relevant product documentation and market-specific device status.
Confirm that staff understand errors, limits and fallback methods.
Agree how updates, incidents and recurring problems will be reviewed.
A product should not be described as 'MHRA approved' or 'GDPR compliant' merely because those words appear in a sales conversation. Ask for the specific documentation and arrangements relevant to your use, and have unresolved questions assessed appropriately.
A useful closing question for the demonstration
Ask the supplier to show a case where the system produces an uncertain, wrong or unusable output, and then demonstrate the normal recovery process. This reveals how the product behaves when the team needs judgement rather than a polished success example.
Frequently asked questions
Is the largest accuracy percentage the best buying criterion? No. Compare like-for-like tasks and include errors, independence of testing and practical consequences.
Can a local pilot replace the supplier's validation? No. It can test usability and local workflow fit, but it cannot substitute for the evidence and governance appropriate to the intended use.
What should the final buying record contain? The intended task, reviewed evidence, outstanding limitations, pilot results, costs and the rationale for the decision.
Does Diamed Engineering endorse a product in this series? These articles explain evaluation questions. They do not rank AI suppliers or promise patient outcomes.
Sources and image credits
Written for Diamed Engineering. Research findings are attributed above; tables, explanations and labelled synthetic examples are original editorial material.
Photographs are credited beside their use and supplied under the Pexels licence. They do not imply endorsement by the pictured people.
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