Prepared by the scanO editorial team. Editorial draft prepared 2 November 2026; independent specialist review pending.
Quick answer: AI dental equipment should perform a clearly defined learned computational task, with evidence appropriate to its intended use. Automation, a digital screen or a chatbot label does not establish clinical AI capability. Ask what input is analysed, what output is produced and what decision the evidence supports.
The term AI can appear on devices that solve very different problems. One analyses radiographs, another photographs and another patient enquiries. The clinic should compare systems by task and evidence, not put them in one ranking because they share a marketing term.
Ask the vendor to identify the input type and the output. An image capture feature is not the same as a model-generated classification. A report layout is not proof that the underlying system evaluated a clinical condition.
Request a sample with the machine output and clinician annotation clearly distinguished. If the system estimates risk, ask what risk means, over which timeframe and in which population. Do not let a vague prediction become a personalised disease forecast without appropriate evidence.
Request the validation report for the exact product and model version. Identify the clinical population, reference standard, external evaluation and limitations. The AI oral lesion research review illustrates why promising research remains tied to the studied systems and conditions, not every marketed product.
A model may perform well on carefully selected images while failing to assess ordinary captures. Ask for the number of people, image-quality exclusions and unable-to-assess rate. Require evidence relevant to the clinic's intended setting rather than a percentage detached from its denominator.
The system should explain its intended role and limitations. Screening flags, suggested annotations and final diagnoses must not be blurred. Ask what happens when the dentist disagrees and whether the record preserves the original output and the clinician's assessment.
Test a case with an uncertain result and a case outside scope. A useful product should have an appropriate boundary, not claim to assess everything. The operational process should tell users when to repeat capture, examine further or use a different pathway.
Connected hardware may receive software and model updates. Ask whether updates change the target conditions, thresholds, report language or workflow. The clinic needs to know how changes are documented and whether staff require retraining.
Keep the version associated with each report where relevant. A later update should not silently rewrite the meaning of an earlier clinical record. Ask how historical reports remain accessible and how the vendor communicates known limitations or incidents.
Run a synthetic record from capture to export. Test roles, connectivity failures, repeat captures and support. A model's classification result is only one part of the product; identification, report delivery and data handling can also fail.
Request the commercial terms, evidence of applicable regulatory status and relevant security documentation. A certification logo without scope is not enough. Do not infer that another country's authorisation covers the proposed use in India.
The scanO air page describes AI-supported photographic screening and visual reports. A clinic should still request the exact version, intended use and evidence relevant to its decision. This article does not convert marketing accuracy claims into independently established performance.
scanO air does not replace a dentist's diagnosis or indicated radiographic assessment in this guide. Nor is it presented as an oral cancer diagnostic device. The demonstration should show how the screening output enters a clinician-led examination and explanation.
Choose a predefined set of ordinary clinic scenarios and document what the AI is expected to do in each. Include good-quality input, poor-quality input, situations outside the intended use, and a case requiring manual escalation. Ask the vendor to state exactly which outputs are screening suggestions, clinical decision support, or administrative automation.
Check what information the operator sees when a result is uncertain, how the clinic can correct an error, and whether an update changes the model or report. A published study of one algorithm or population does not validate a separate marketed product. Request product-specific evidence, version information, limitations and post-deployment support commitments.
Clinical gate: the proposed use is appropriate, evidence is understood and a dentist retains clinical responsibility. Operational gate: real staff can complete the workflow and handle failures. Commercial gate: ongoing cost, service and data portability are acceptable. Document all three outcomes independently. A good demonstration cannot replace one failed mandatory gate.
First define the bottleneck, such as inconsistent visual documentation. Then ask the vendor to demonstrate that task using your acceptance criteria. Review evidence and data terms before negotiating the commercial scope. Finally, run a supervised implementation with a fallback and named owner.
Do not buy a system only because a competitor has one. A clinic may benefit more from better lighting, reliable records or a communication protocol before adding AI. Use the AI software category guide to separate clinical applications and the general AI dentistry guide for broader context.
QuestionSatisfactory evidenceWarning signDefined input/output?Specific data type, task and sample outputAI label with no defined computationValidated for this use?Product/version-specific study and limitationsUnrelated paper or unexplained percentageClinician control?Clear review, disagreement and uncertainty pathwayEvery flag presented as treatment needUpdates traceable?Version/change documentation and record continuitySilent changes without explanationWhole workflow verified?Synthetic capture-to-export and failure testDemonstration only on curated successful cases
Decision record: intended clinic task; evidence received; unanswered question; mandatory safeguard; pilot measure; responsible reviewer; accept/retest/reject. Do not create a numerical readiness score from missing evidence.
For further practical context, see dental equipment planning checklist and clinic setup budget worksheet. These resources support planning; adapt decisions to your clinic and review clinical requirements with qualified professionals.
