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ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model
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 na...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382494/ https://www.ncbi.nlm.nih.gov/pubmed/37507396 http://dx.doi.org/10.1038/s41368-023-00239-y |
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author | Huang, Hanyao Zheng, Ou Wang, Dongdong Yin, Jiayi Wang, Zijin Ding, Shengxuan Yin, Heng Xu, Chuan Yang, Renjie Zheng, Qian Shi, Bing |
author_facet | Huang, Hanyao Zheng, Ou Wang, Dongdong Yin, Jiayi Wang, Zijin Ding, Shengxuan Yin, Heng Xu, Chuan Yang, Renjie Zheng, Qian Shi, Bing |
author_sort | Huang, Hanyao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10382494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103824942023-07-30 ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model Huang, Hanyao Zheng, Ou Wang, Dongdong Yin, Jiayi Wang, Zijin Ding, Shengxuan Yin, Heng Xu, Chuan Yang, Renjie Zheng, Qian Shi, Bing Int J Oral Sci Review Article 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. Nature Publishing Group UK 2023-07-28 /pmc/articles/PMC10382494/ /pubmed/37507396 http://dx.doi.org/10.1038/s41368-023-00239-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Article Huang, Hanyao Zheng, Ou Wang, Dongdong Yin, Jiayi Wang, Zijin Ding, Shengxuan Yin, Heng Xu, Chuan Yang, Renjie Zheng, Qian Shi, Bing ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model |
title | ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model |
title_full | ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model |
title_fullStr | ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model |
title_full_unstemmed | ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model |
title_short | ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model |
title_sort | chatgpt for shaping the future of dentistry: the potential of multi-modal large language model |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382494/ https://www.ncbi.nlm.nih.gov/pubmed/37507396 http://dx.doi.org/10.1038/s41368-023-00239-y |
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