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REVIEW article

Front. Dent. Med
Sec. Systems Integration
Volume 5 - 2024 | doi: 10.3389/fdmed.2024.1456208

Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Care

Provisionally accepted
Mohsen Ahmadi Mohsen Ahmadi 1*Masoumeh Farhadi Masoumeh Farhadi 2Elyas Irankhah Elyas Irankhah 2
  • 1 Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, United States
  • 2 University of Massachusetts Lowell, Lowell, Massachusetts, United States

The final, formatted version of the article will be published soon.

    Artificial intelligence has dramatically reshaped our interaction with digital technologies, ushering in an era where advancements in AI algorithms and Large Language Models (LLMs) have natural language processing (NLP) systems like ChatGPT. This study delves into the impact of cutting-edge LLMs, notably OpenAI's ChatGPT, on medical diagnostics, with a keen focus on the dental sector. Leveraging publicly accessible datasets, these models augment the diagnostic capabilities of medical professionals, streamline communication between patients and healthcare providers, and enhance the efficiency of clinical procedures. The advent of ChatGPT-4 is poised to make substantial inroads into dental practices, especially in the realm of oral surgery. This paper sheds light on the current landscape and explores potential future research directions in the burgeoning field of LLMs, offering valuable insights for both practitioners and developers. Furthermore, it critically assesses the broad implications and challenges within various sectors, including academia and healthcare, thus mapping out an overview of AI's role in transforming dental diagnostics for enhanced patient care.

    Keywords: dental, diagnosis, ChatGPT, artificial intelligence, LLM, nlp, Patient Care

    Received: 02 Jul 2024; Accepted: 16 Oct 2024.

    Copyright: © 2024 Ahmadi, Farhadi and Irankhah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mohsen Ahmadi, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, 33431, Florida, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.