How AI Is Changing Intraoral Scanning Workflows

Introduction

Artificial intelligence is becoming part of the conversation around digital dentistry, but a useful discussion should begin with the workflow rather than a product promise. An intraoral scan still depends on patient preparation, moisture control, operator movement, complete data capture, and clinical review. AI may help organize or assess parts of that process, yet it does not remove the need for a clinician to decide whether the scan is suitable for the intended treatment.

Besmile's digital dental portfolio includes intraoral scanning, CAD/CAM equipment, dental materials, 3D printing, and related digital solutions. The practical question for a clinic is how any intelligent software function fits into this chain. It should make the scan easier to review, make missing information more visible, or help the operator follow a consistent process without turning an unverified software capability into a clinical guarantee.

AI-assisted scan recognition

Scan recognition can help organize digital information by distinguishing areas of the dentition or by presenting the captured anatomy in a way that is easier to review. In a clinical workflow, that kind of assistance is useful only when the operator can understand what has been identified and can check whether the result matches the patient and the intended case.

Recognition should therefore be treated as a review aid. The operator still needs to confirm the preparation, margins, occlusal information, adjacent structures, and any other areas required for the case. If the software highlights or labels an area incorrectly, the error must be corrected before the file moves into design or production.

Automatic soft tissue filtering

Soft tissue, saliva, blood, and movement can affect the clarity of an intraoral scan. An intelligent workflow may help the operator identify unwanted data or separate areas that are not part of the required digital model. This can make data review more efficient, but filtering should not be treated as permission to scan without proper isolation or preparation.

The clinical team should compare the filtered result with the unfiltered capture when the case is difficult. A useful system makes it possible to see whether relevant anatomy has been removed along with unwanted data. Moisture control, retraction, patient positioning, and a stable scanning path remain important parts of the workflow regardless of the software assistance available.

Real-time error detection

One potential role for AI is to make possible gaps or inconsistencies visible while the operator is still scanning. Earlier feedback may help the team decide whether to revisit an area before the appointment ends. This is particularly relevant when the missing information would otherwise be discovered only during design or laboratory review.

Real-time guidance should be validated against the actual case requirements. A scan can appear complete on screen while still lacking a margin detail or occlusal relationship needed for the restoration. The final decision should remain with the clinical and technical team, using the case prescription and the intended downstream workflow as the reference.

Improved margin line capture

Margin visibility is a key part of restorative scanning. Software assistance may help the operator inspect or review a proposed margin line, but the system cannot replace the need for a visible and accurately captured preparation. Subgingival margins, bleeding, saliva, and limited access can still make the clinical situation difficult.

A consistent margin review can include checking the proposed line against the preparation, looking for gaps or ambiguous areas, and recapturing data when the margin is not clear. The practice should document how that review is performed and communicate any limitation to the laboratory before the design begins.

Faster learning curves for new users

New users need more than a fast device. They need to understand scanning paths, moisture control, data completeness, patient movement, and the review standards used by the clinic or laboratory. Intelligent prompts or visual feedback may support training if the trainee can connect the feedback to a clear scanning rule.

Training should combine guided practice with independent review. The operator should learn to recognize when the software is uncertain, when manual inspection is needed, and when a scan should be repeated. A shorter learning curve should be demonstrated through the clinic's own quality checks rather than assumed from the presence of an AI label.

M5 Pro intelligent workflow features

The M5 Pro is included in Besmile's intraoral scanning range. When a clinic evaluates the M5 Pro in an intelligent or AI-supported workflow, it should separate verified product functions from general expectations about AI. The relevant checks are whether the scanner captures the required case data, whether the review process is clear, and whether the exported file fits the software and CAD/CAM route used by the clinic or laboratory.

A practical evaluation can use representative single-unit, multi-unit, and challenging cases without assuming that one result applies to every patient. The team can record scan completeness, retakes, review findings, transfer issues, and the time required for training. This produces workflow evidence while avoiding unsupported claims about automatic recognition, filtering, or clinical accuracy.

Future AI trends in digital dentistry

Future AI development may focus on making digital records easier to organize, helping teams review scan quality, supporting communication between clinical and laboratory stages, and connecting digital data with CAD/CAM production. These possibilities are relevant to a company whose portfolio spans scanning, materials, equipment, and digital dental solutions.

The adoption question should remain practical. Clinics need to know what the system does, what data it uses, how the operator can correct it, where the information is stored, and which decisions still require human review. Clear documentation and controlled testing are more useful than broad claims that AI will solve every scanning or production problem.

Conclusion

AI can change the way intraoral scanning workflows are reviewed and supported, but it does not remove the clinical responsibilities around preparation, capture, verification, and communication. Besmile's digital dental solutions can be evaluated as part of a connected workflow that includes the M5 Pro, CAD/CAM equipment, materials, and production steps. The safest measure of value is whether the complete process produces more understandable, reviewable, and usable digital records for the cases the clinic actually treats.