# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 1 Introduction  
Artificial intelligence (AI) is a field of computer science that involves developing algorithms capable of performing tasks that would normally require human intelligence [1]. These tasks include recognizing patterns and images, making decisions based on complex data, understanding and translating languages, and the ability to respond to questions or voice commands [2, 3]. One of the most significant developments in AI application in recent years has been the creation and release of ChatGPT by OpenAI [[https://openai.com](https://openai.com)]([https://openai.com](https://openai.com)) (accessed on 1 October 2024) (San Francisco, CA, USA). ChatGPT is a large language model based on Generative Pre-trained Transformer (GPT) technology, which uses deep learning techniques to produce text that can mimic human style on a large scale. ChatGPT was launched as a trial version in November 2022 and later made publicly available, gaining immediate attention for its ability to generate coherent and contextually appropriate responses across various fields of knowledge and everyday life [4]. The technology behind ChatGPT, and moregenerally all GPT models, operates through what is known as transfer learning. Initially, the model is pre-trained on a vast dataset of publicly available texts; this phase is not specific to a task but aims to develop a general understanding of language. Subsequently, it undergoes a refinement process, during which the model is further trained on a more specific text corpus (for example, medical texts) to make its responses more appropriate to certain applications [5,6]. Furthermore, the AI algorithm uses machine learning (ML) and natural language processing techniques to identify meaningful patterns and understand user needs [7]. On the other end, the user applies prompt engineering, a process that guides AI systems to produce desired results. Specifically, it involves writing, refining, and optimizing the starting data (prompts) to encourage AI systems to create specific outputs. Generative AI systems are designed to generate specific outputs based on the quality of the instructions provided, thus following the rule that quality instructions determine quality results, minimizing bias. The objective is to create queries that help generative AI models understand the language and every nuance to reach a specific goal. Of course, creating unbiased prompts is crucial for ensuring the fairness and effectiveness of the tool. This can be achieved by keeping the prompt clear and specific, providing sufficient context and balancing specificity and generality. Knowing if the training dataset is diverse enough would also reduce the risk of biases. The applications of AI are numerous, from autonomous vehicle driving to customizing online shopping experiences, advanced robotics, and financial analysis [8]. AI has shown remarkable potential by being applied across all branches of the medical sector: in 2019, a team of researchers from MIT ([[https://www.mit.edu](https://www.mit.edu)]([https://www.mit.edu](https://www.mit.edu)) accessed on 1 October 2024) and Harvard Medical School ([[https://hms.harvard.edu](https://hms.harvard.edu)]([https://hms.harvard.edu](https://hms.harvard.edu)) accessed on 1 October 2024) developed an AI algorithm that could predict the risk of developing breast cancer up to five years before it clinically manifests, using mammograms and analyzing subtle patterns in breast tissue that are invisible to the human eye [9]. Different artificial intelligence models were developed using ultrasound data, patient histories, and medical records to diagnose polycystic ovary syndrome, allowing for earlier and more accurate diagnosis compared to traditional methods based solely on clinical and hormonal analysis [10].