Hidden Side of Dental AI | 医療法人社団EPSDC

Introduction
You may hear news saying “Medical AI is making amazing progress,” but wonder, “Why isn’t AI used much at the dentist?” In fact, there are several hurdles to making AI truly helpful for root canal treatment. Here are the five main walls explained in simple terms.
Wall 1: There aren’t nearly enough images collected

AI gets better when it practices with lots of high-quality data.
In cancer imaging, there are giant databases with tens of millions of images used worldwide.
But in dentistry—especially root canal care—public datasets are very limited. Many studies rely on small, single-clinic collections with only hundreds to a few thousand images, often with skewed age or regional balance.
As a result, the AI may fail when used at a different hospital or in a different region (poor generalization).

Wall 2: Converting images to JPEG/PNG erases important information

Medical images are stored in a special format called DICOM. It contains metadata like scan settings, resolution, and device type—details AI needs to interpret images correctly.
To save space or make handling easier, people sometimes convert DICOM to JPEG or PNG.
That removes crucial metadata and makes it unclear “under what conditions” the image was taken. Accuracy drops and results can’t be reproduced elsewhere.
In short, it’s very important to use the original DICOM data as-is.

Wall 3: When the machine changes, AI gets lost (device bias)

CBCT machines for 3D dental imaging differ by manufacturer and model, each with its own “look” (brightness, contrast, etc.).
AI can accidentally learn not only tooth structures but also those device-specific quirks. Then it gets confused by images from other brands. This is called domain shift.
In some studies, an AI trained on brand A failed on brand B, and only special methods (e.g., adjusting differences in the frequency/Fourier domain) helped.
We need techniques that “normalize” across many machines.

Wall 4: Commercial AI training data is often a black box

Some dental AI tools are already on the market (e.g., for caries or apical lesion detection), and a few even have regulatory clearance.
But for business reasons, many don’t disclose what data they used for training and testing.
Public hints show very few diseased cases or tiny test sets.
Without independent verification, it’s hard to know if the AI is truly reliable across different patients and devices.

Wall 5: There are traps in how datasets are built

Some public dental datasets have quality issues.
Example 1: They mix many “synthetic X-rays” computed from 3D volumes instead of real radiographs. These may look similar but don’t capture true-world distortions and overlaps.
Example 2: They include mainly diseased images, exclude healthy ones, and inflate dataset size by rotating/flipping the same images.
In real clinics, recognizing “normal” is crucial. If AI learns mostly disease, it tends to overcall disease in healthy people (more false positives).

Summary and the road ahead

To trust dental AI (especially in root canal care), we need not just “more data,” but “data collected and managed the right way.”
Three pillars:
- Data standardization: Keep DICOM and metadata intact so results are reproducible anywhere.
- Data stewardship: Share safely under clear rules so data can be found and reused responsibly.
- Bias mitigation: Balance age, region, disease severity, and device brands so AI works reliably across settings.
With these three pillars, AI can grow from a “research-only tool” into a “reliable partner in real clinics.”
Clinicians and engineers should always consider the data quality and biases behind AI results—and how much we truly understand and trust them.

Reference

Thakkar et al. 2026 The Fundamental Necessities of Comprehensive Dental Repositories to Support the Generalisation of AI Applications in Endodontics. International Endodontic Journal, 2026; 0:1–9

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