From Precision to Intelligence: The Evolution of Intraoral Scanning

Intraoral scanning has already earned a well-validated place in routine restorative workflows, and its remaining accuracy challenges are more focused at full-arch and implant level, where soft-tissue mobility and inter-implant span length most tax current stitching algorithms. Studies show that IOS cannot capture subgingival margins well, and even less if covered with blood, saliva, or soft tissue.^4,5 Undercuts also present a particular challenge for an IOS. Conventional impressions allow the impression material to flow into the undercuts and show advantages regarding the reproduction of subgingival finishing lines; therefore, they are often used for subgingival preparations. These limitations remain the main reason why full-arch and implant accuracy continues to attract significant attention nearly 10 years after IOS technology has become common in clinical practice.⁶

For full-arch digital impressions, IOSs generate an image by stitching together hundreds of sequential images using best-fit alignment algorithms, and these algorithms perform best when abundant still, geometric references are available.⁷ Difficulties occur when stitching images of smooth, soft tissue from edentulous ridges, and the resulting stitching errors accumulate as the scanning span lengthens. The main reason is that the oral mucosa is constantly displaced during digital impressions and is difficult to keep stable.⁸ Additionally, anatomical irregularities and varying tissue colors in the scanning area of completely edentulous cases are often inadequate.⁹ Photogrammetry has also been developed to transfer implant positions more accurately from the patient’s mouth to a virtual cast.

A 2025 systematic review and meta-analysis comparing full-arch intraoral scans with conventional impressions concluded that most studies evaluating clinical complete denture performance from digital impressions still fall within clinically acceptable accuracy thresholds.⁷ Another systematic review and meta-analysis on full-arch implant impressions found that IOS accuracy outperformed conventional impressions in angled implant scenarios, while conventional impressions were more beneficial when implants were parallel; accuracy also varied significantly by scanner brand.³ These findings reinforce that digital and conventional workflows each have scenario-specific advantages, and based on the clinician’s experience with both impression techniques, digital impressions can be as effective as traditional methods for full-arch cases when adequate retraction is used, a proper intraoral environment is established, and the recommended scanning protocols are followed.

In edentulous spans, stitching errors primarily occur due to the lack of stable geometric references; a recent technique uses auxiliary clips designed by AI that attach to existing implant scanbodies to bridge edentulous spans, providing stable references that aid the scanning process.¹⁰ These strategies demonstrate that enhancing full-arch accuracy depends not only on software but also on carefully engineered hardware, whether traditional or AI-designed.⁹

In clinical decision-making, selecting a specific impression method requires careful assessment of several factors, including scanning patterns, operator experience, ambient lighting, and environmental conditions such as temperature and humidity.

AI Inside the Scanning Software

Recently, AI has gained popularity in medicine because of its ability to automate tasks that simulate human intelligence. In dentistry, AI-based software programs have been developed for different applications. AI includes machine capabilities that aim to replicate human cognitive functions, such as learning and problem-solving. The most prominent development is AI integrated directly into scanning and segmentation software. Automated tooth segmentation, which was traditionally manual or semi-automatic in digital workflows, is one of the earliest and most successful uses of AI.¹¹ Other AI-based applications are integrated into digital data acquisition and implant planning tools, including file alignment, scan cleaning, noise reduction, structure segmentation, airway analysis in CBCT images, automatic implant placement, and surgical guide design, but further research is needed to evaluate their accuracy and efficiency.¹² In digital impressions, AI improves IOS accuracy by addressing mesh defects and supporting more precise digital workflows. Among other AI techniques, deep learning with convolutional neural networks is considered the most effective for image analysis.¹¹

AI is also being built into the real-time scan-acquisition process itself, not only into post-scan processing. An in vitro study comparing a scanner’s AI-assisted mode with its conventional mode across simulated complete-arch cases found that disabling the AI feature improved trueness in most measured areas, while enabling it created more precision and accuracy in arch distortion and whole-arch deviation, the parameters most responsible for full-arch misfit.¹³ This result suggests that using “AI-enhanced” scanning as a uniform improvement over conventional scanning appears to be scenario-dependent, focused on the full-arch dimensions that have always been IOS technology’s weakest point, rather than uniformly superior across all measures of accuracy.

An Evolving Technology

Advances in digital acquisition technologies have enabled the integration of data to create a 3-dimensional virtual patient. Digital technologies such as facial scanners, photogrammetry techniques, IOSs, and jaw tracking systems aim to enhance efficiency when compared with traditional methods for planning, designing, and manufacturing implant-supported prostheses.¹⁴ Understanding these technologies, workflows, and the factors influencing their accuracy is essential for their successful implementation, and dental professionals remain responsible for diagnosing and treating patients based on their clinical judgment and expertise, regardless of AI support. Scientific communities must also take responsibility for regulating and overseeing the use of these AI technologies.¹²

Evidence shows that intraoral scanning is now common in regular restorative procedures, although it still has limitations with full-arch and implant-supported scenarios. Tools addressing these limitations, from engineered auxiliary hardware to AI-driven segmentation and AI-assisted real-time acquisition, each target a different link in the digital impression chain, and open-platform systems, often powered by AI, increasingly combine them with cone-beam computed tomography and facial scanning for diagnosis, smile design, and treatment planning.⁶ Clinicians should assess each AI or auxiliary hardware feature based on its stated accuracy improvements—rather than assuming that “AI-enabled” automatically means “more accurate”—and keep clinical judgment at the center of every digital workflow. As precision gives way to intelligence, digital tools in dentistry remain instruments whose value depends on the clinician who leads them.