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

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          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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          Most cited references62

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            BioBERT: a pre-trained biomedical language representation model for biomedical text mining

            Abstract Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.
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              Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

              We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.
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                Author and article information

                Contributors
                huanghanyao_cn@scu.edu.cn
                ouzheng1993@knights.ucf.edu
                Journal
                Int J Oral Sci
                Int J Oral Sci
                International Journal of Oral Science
                Nature Publishing Group UK (London )
                1674-2818
                2049-3169
                28 July 2023
                28 July 2023
                2023
                : 15
                : 29
                Affiliations
                [1 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, , Sichuan University, ; Chengdu, China
                [2 ]GRID grid.170430.1, ISNI 0000 0001 2159 2859, Department of Civil, Environmental & Construction Engineering, , University of Central Florida, ; Orlando, USA
                [3 ]GRID grid.170430.1, ISNI 0000 0001 2159 2859, College of Transportation Engineering, , University of Central Florida, ; Orlando, USA
                [4 ]GRID grid.263901.f, ISNI 0000 0004 1791 7667, School of Transportation and Logistics, , Southwest Jiaotong University, ; Chengdu, China
                [5 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, C2SMART Center, Tandon School of Engineering, , New York University, ; Brooklyn, USA
                [6 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Eastern Clinic, West China Hospital of Stomatology, , Sichuan University, ; Chengdu, China
                Author information
                http://orcid.org/0000-0002-6805-0157
                http://orcid.org/0000-0001-6313-8566
                Article
                239
                10.1038/s41368-023-00239-y
                10382494
                37507396
                0b2cdcad-9209-497c-8e60-d97958534120
                © The Author(s) 2023

                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/.

                History
                : 26 March 2023
                : 6 July 2023
                : 13 July 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100012542, Sichuan Provincial Department of Science and Technology | Sichuan Province Science and Technology Support Program;
                Award ID: 2022NSFSC0743
                Award Recipient :
                Funded by: This work was supported by the Research and Development Program, West China Hospital of Stomatology, Sichuan University (RD-02-202107), Sichuan Province Science and Technology Support Program (2022NSFSC0743), and Sichuan Postdoctoral Science Foundation (TB2022005).
                Categories
                Review Article
                Custom metadata
                © West China School of Stomatology Sichuan University 2023

                Dentistry
                dentistry,electrodiagnosis
                Dentistry
                dentistry, electrodiagnosis

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