AI in dental radiography — past the hype, into the daily workflow

What AI genuinely contributes to caries and periapical detection, where its limits are, and how it should sit inside a clinic's workflow.

AI in dentistry has moved past the marketing phase into routine use — but it only helps when it sits in the right place in the workflow: a fast suggester, never the decision-maker.

What it’s genuinely good at

• Finding-level screening: flagging suspicious interproximal caries and periapical lesions on periapicals and bitewings — a tireless second pair of eyes.
• Report drafting: structured findings, impression, differential and recommendations — for the clinician to edit, not to rubber-stamp.
• Chart integration: the decisive step. A suggested finding should land directly on the right tooth and surface of the odontogram, one confirmation tap away from the record.
• Clinical scribing: turning rough visit notes into a structured SOAP note.

The honest limits

Models are sensitive to image quality, fail on atypical presentations, and the legal responsibility for diagnosis stays with the dentist. AI output must always carry a “verify clinically” label and should never enter the record without explicit confirmation.

The infrastructure question

The world’s best model is useless if sanctions or filtering cut access to it. AI infrastructure must be reachable from where the clinic actually operates, and the service key must stay server-side — an API key embedded in a desktop app is extractable and unrotatable.

In DentanYar, radiograph analysis and the clinical scribe run through region-appropriate AI services via a server-side proxy; suggested findings land on the odontogram for one-tap confirmation, and nothing enters the patient record without the dentist’s sign-off.