# ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model

arXiv: https://arxiv.org/abs/2304.03086 · HTML: https://mlsanity.com/paper/2304.03086/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 37 of its 111 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 30 of 37)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Lessons from Natural Language Inference in the Clinical Domain](https://mlsanity.com/paper/1808.06752/) (2018)
- [Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective](https://mlsanity.com/paper/2003.00330/) (2020)
- [MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare](https://mlsanity.com/paper/1810.09593/) (2018)
- [OntoProtein: Protein Pretraining With Gene Ontology Embedding](https://mlsanity.com/paper/2201.11147/) (2022)
- [MMBERT: Multimodal BERT Pretraining for Improved Medical VQA](https://mlsanity.com/paper/2104.01394/) (2021)
- [UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus](https://mlsanity.com/paper/2010.10391/) (2020)
- [Paragraph-level Simplification of Medical Texts](https://mlsanity.com/paper/2104.05767/) (2021)
- [Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition](https://mlsanity.com/paper/2010.03746/) (2020)

## Built on by (most cited first, 30 of 40)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02104/) (2024)
- [Large Language Models for Medicine: A Survey](https://mlsanity.com/paper/2405.13055/) (2024)
- [Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment](https://mlsanity.com/paper/2312.11813/) (2023)
- [Generative AI for Synthetic Data Generation: Methods, Challenges and the Future](https://mlsanity.com/paper/2403.04190/) (2024)
- [Model-as-a-Service (MaaS): A Survey](https://mlsanity.com/paper/2311.05804/) (2023)
- [TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model](https://mlsanity.com/paper/2404.01273/) (2024)
- [Weakly Supervised Detection of Hallucinations in LLM Activations](https://mlsanity.com/paper/2312.02798/) (2023)
- [ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework](https://mlsanity.com/paper/2403.00781/) (2024)
- [Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training](https://mlsanity.com/paper/2404.01157/) (2024)
- [From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents](https://mlsanity.com/paper/2409.03512/) (2024)
- [DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice](https://mlsanity.com/paper/2509.23344/) (2025)
- [How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems](https://mlsanity.com/paper/2403.01757/) (2024)
- [Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries](https://mlsanity.com/paper/2305.10163/) (2023)
- [Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis](https://mlsanity.com/paper/2403.16303/) (2024)
- [Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review](https://mlsanity.com/paper/2311.01918/) (2023)
- [From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice](https://mlsanity.com/paper/2410.01812/) (2024)
- [A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI](https://mlsanity.com/paper/2307.15425/) (2023)
- [OralGPT-Plus: Learning to Use Visual Tools via Reinforcement Learning for Panoramic X-ray Analysis](https://mlsanity.com/paper/2603.06366/) (2026)
- [Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach](https://mlsanity.com/paper/2311.07377/) (2023)
- [Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data](https://mlsanity.com/paper/2403.04785/) (2024)
- [Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Care](https://mlsanity.com/paper/2406.06616/) (2024)
- [Video-QTR: Query-Driven Temporal Reasoning Framework for Lightweight Video Understanding](https://mlsanity.com/paper/2512.09354/) (2025)
- [Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model](https://mlsanity.com/paper/2310.20357/) (2023)
- [Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges](https://mlsanity.com/paper/2402.11203/) (2024)
- [FastRM: An efficient and automatic explainability framework for multimodal generative models](https://mlsanity.com/paper/2412.01487/) (2024)
- [Investigating Vision-Language Model for Point Cloud-based Vehicle Classification](https://mlsanity.com/paper/2504.08154/) (2025)
- [The Impact of Performance Expectancy, Workload, Risk, and Satisfaction on Trust in ChatGPT: Cross-sectional Survey Analysis](https://mlsanity.com/paper/2311.05632/) (2023)
- [Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning](https://mlsanity.com/paper/2402.15755/) (2024)
- [SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes](https://mlsanity.com/paper/2506.06649/) (2025)

## Abstract (arXiv)

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.