Transformer-based models in dentistry: a systematic review
Xuhan Zhu, Mingwei Zhou, Masahiro Shimogishi, Eriko Marukawa, Xin Zhu · 2026
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Description
Xuhan Zhu leads a team reviewing transformer models in dentistry, in a 2026 systematic review. Transformers are AI networks that weigh context across a whole image, a strength over older local methods. Dental uses have spread fast, but no full synthesis of their worth existed.
The search gathered 112 studies from 2020 to 2025. Most used hybrids that pair classic image networks with transformer parts; a smaller set used pure transformers. Hybrids beat pure models across tooth mapping, decay spotting, implant placement, and jaw assessment tasks. Scores dropped five to eight points when models faced outside data, a warning about real-world fit. The authors cite small labeled data sets and uneven test methods as the main brakes on clinic use.
Catalog record
| CREATOR | Xuhan Zhu, Mingwei Zhou, Masahiro Shimogishi, Eriko Marukawa, Xin Zhu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 170440 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |