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Review
. 2023 Jul 28;15(1):29.
doi: 10.1038/s41368-023-00239-y.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

Hanyao Huang #  1 , Ou Zheng #  2 , Dongdong Wang  3 , Jiayi Yin  4 , Zijin Wang  3 , Shengxuan Ding  5 , Heng Yin  4 , Chuan Xu  6   7 , Renjie Yang  8 , Qian Zheng  4 , Bing Shi  4
Affiliations
Review

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

Hanyao Huang et al. Int J Oral Sci. .

Abstract

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Examples of a text mining application for extracting a patient’s historical record with an LLM like ChatGPT. Keywords are highlighted in red
Fig. 2
Fig. 2
Examples of natural language reasoning (NLR) application of medication suggestions derived from a patient’s record. Keywords are highlighted in red
Fig. 3
Fig. 3
Example of a narrative output of the Patient’s record generated from keywords with NLG
Fig. 4
Fig. 4
Framework of generating synthetic quasi-EHRs data by LLMs
Fig. 5
Fig. 5
Schematic of dental condition diagnosis with a vision-language model of ALBEF
Fig. 6
Fig. 6
A VQA example framework with the assistance of BLIP-2
Fig. 7
Fig. 7
2D semantic segmentation with 3D reconstruction for lesion identification
Fig. 8
Fig. 8
Example of visual data generation
Fig. 9
Fig. 9
Example of the audio waveform and spectrogram analysis in speaking a “baba” and b “mama” with TorchAudio of normal people and patients with velopharyngeal insufficiency
Fig. 10
Fig. 10
Schematic of audio-language assisted diagnosis based upon audio waveform and spectrogram analysis with TorchAudio
Fig. 11
Fig. 11
Concept of automatic multi-modal LLM AI system for dentistry application
Fig. 12
Fig. 12
Application of the multi-modal LLM AI system in dental caries
See this image and copyright information in PMC

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