AI and aligners: Efficiency without losing clinical oversight

When AI performs best

AI performs extremely well when the task is based on objective and measurable criteria. For example, AI systems have already demonstrated impressive accuracy in detecting caries on radiographs. In such cases, the question is relatively clear: is a lesion present or not? If so, how extensive is it?

These are classification tasks with defined parameters. AI systems are particularly good at recognising patterns in data. When the criteria are objective and binary, AI can often match or even exceed human performance. The same principle applies to some aspects of orthodontic diagnostics. Identifying deviations from defined occlusal relationships or anatomical norms can be approached computationally. However, the situation changes dramatically when we move from diagnosis to treatment planning.

“Digital simulations that look perfect do not always translate into predictable clinical outcomes.”

Treatment planning is not a binary problem

Orthodontic treatment planning rarely has a single correct answer. Consider a typical aligner case involving moderate crowding. Several treatment strategies may be possible:

• Should the clinician expand the arch or perform interproximal reduction?
• Should incisor alignment prioritise aesthetics or occlusal stability?
• Should minor compromises be accepted in order to shorten treatment time?

These questions involve judgement and clinical philosophy. Even experienced clinicians may propose different solutions for the same case. AI cannot independently resolve these trade-offs in the way a clinician does. Instead, it identifies patterns within historical datasets and generates statistically optimised solutions. But statistical optimisation is not the same as clinical decision-making.

The data behind AI systems

Modern aligner systems are trained on enormous datasets drawn from previous treatments. At first glance, this seems like a major advantage. More data should mean more accurate predictions. However, the nature of the data matters as much as the quantity. Large aligner databases include cases treated by thousands of clinicians with very different experience levels, treatment philosophies and finishing standards. Some clinicians carefully review every treatment set-up. Others rely heavily on automated proposals.

As a result, the dataset from which AI learns may contain significant variation in quality and clinical strategy. AI does not evaluate these differences in the way clinicians do. It simply identifies patterns within the data it receives. If the underlying data reflects variation in clinical experience, treatment philosophy and finishing standards, the resulting recommendations may also reflect that inconsistency. This is one of the reasons why digital treatment set-ups should always be interpreted critically by the clinician.

When digital models meet biological reality

Another important limitation arises from the difference between digital models and biological systems. Aligner treatment planning begins with digital models created from intra-oral scans. Before tooth movement can be simulated, the software must determine where one tooth ends and the next begins. This process of tooth segmentation is partly based on estimation. In areas where the scan data is incomplete or ambiguous, the software must interpolate missing data or smooth the digital surface. Small assumptions made during this step can influence the virtual geometry of the teeth and therefore the predicted movement patterns.

While the digital model may appear mathematically precise, it is still an approximation of biological reality. Once the treatment simulation begins, additional layers of abstraction are introduced: movements are staged digitally, attachments are suggested automatically and collisions between teeth are calculated digitally. Each step contributes to the final animation that the clinician sees on screen. However, teeth do not move because an animation shows them moving; teeth move because forces are applied within biological limits. That distinction is critical.

The hidden role of biomechanics

One of the most under-estimated aspects of digital aligner planning is biomechanics. In many digital simulations, individual teeth can appear to move independently. A premolar is rotated. An incisor is intruded. The arch is expanded. In reality, orthodontic movement never occurs in isolation. Every force applied to a tooth produces reactive forces elsewhere in the system. Aligners distribute forces across the entire dentition. Anchorage must be managed carefully, and movement of one tooth inevitably affects others. When digital set-ups present movements as independent events, it can create the illusion that biomechanics are simpler than they actually are.

The clinical consequences often appear later during treatment. Anchorage may be lost unexpectedly, vertical control may be lost or teeth may fail to track as predicted. These are not necessarily failures of the software. They are expressions of biological and mechanical complexity.

“Statistical optimisation is not the same as clinical decision-making.”