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Artificial intelligence

Natural language processing in dentistry

Key points

  • Natural language processing provides promising chances for dentistry, in particular for patient communication.

  • Generalisability, reproducibility and explainability must be improved for sustainable implementation.

  • Users must be aware of model capabilities and limitations.

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  3. Nadeem M, Bethke A, Reddy S. StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp 5356-5371. Toronto: Association for Computational Linguistics, 2021.

  4. Xiao Y, Wang W Y. On Hallucination and Predictive Uncertainty in Conditional Language Generation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. pp 2734-2744. Toronto: Association for Computational Linguistics, 2021.

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Corresponding authors

Correspondence to Martha Büttner or Falk Schwendicke.

Ethics declarations

Falk Schwendicke is a co-founder of a Charité startup on dental image analysis. The conduct, analysis and interpretation of this study and its findings was unrelated to this.

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Büttner, M., Schwendicke, F. Natural language processing in dentistry. Br Dent J 234, 753 (2023).

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