ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model
Open accessAbstract
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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Recent studies on M-LLMs in health care have limited exploration of possible solutions or ongoing research aimed at overcoming technical barriers.
“Third, although the challenges of integrating diverse data types into M-LLMs are recognized, there is limited exploration of possible solutions or ongoing research aimed at overcoming these technical barriers [38,39].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [39]
The “Multi-modal LLM” section (page unavailable) describes ongoing research combining images, audio, and text, while acknowledging limited data availability. The “Cross-modal dental diagnosis with LLMs” section (page unavailable) also explores a way to combine text and images for dental diagnosis. The paper therefore does not support the attributed lack of exploration of solutions or ongoing research. (LLM, conf=0.85)
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Experimental — accuracy not yet measuredRecent studies on M-LLMs in health care have a restricted range of data modalities examined, often limited to text, images, videos, and audio.
“First, the range of data modalities examined is often restricted to text, images, videos, and audio [38], with some studies focusing narrowly on a limited number of clinical applications [39].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [39]
In “Cross-modal dental diagnosis with LLMs” (page unavailable), the paper says most dental AI studies have used image-only approaches, supporting the point that modalities studied have been limited. “Multi-modal LLM” (page unavailable) discusses text, images, and audio in medical learning, but the paper does not establish that health-care M-LLM studies are often restricted to the four listed modalities, including video. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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- Authors
- Huang, H., Zheng, O., Wang, D., Yin, J., Wang, Z., Ding, S., Yin, H., Xu, C., Yang, R., Zheng, Q., & Shi, B.
- Affiliations
- Sichuan University (CN)
- University of Central Florida (US)
- Southwest Jiaotong University (CN)
- Corresponding author
- Huang, Hanyao
- Zheng, Ou
- ORCID
- Venue
- International Journal of Oral Science
- Published
- 2023-07-28
- Volume
- 15
- Issue
- 1
- Pages
- 29-29
- ISSN
- 1674-2818 (ISSN-L)
- 2049-3169
- PMC
- PMC10382494
- OpenAlex
- W4385346108
- Cited by
- 324
- References
- 97
- Citations by year
- 2026: 68 · 2025: 119 · 2024: 107 · 2023: 30
- FWCI
- 3.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Oral Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- Sichuan Provincial Postdoctoral Science Foundation (TB2022005)
- Sichuan University (RD-02-202107)
- Sichuan Province Science and Technology Support Program (2022NSFSC0743)
- West China Hospital, Sichuan University
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Dentistry › Oral Surgery › Dental Radiography and Imaging
- Keywords
- ChatGPT, large language models, multimodal large language models, dental diagnosis, automated diagnosis, natural language reasoning, clinical decision support, data privacy, model bias, data quality
- MeSH terms
- Artificial Intelligence, Dentistry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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