Artificial intelligence chatbots in endodontic education-Concepts and potential applications.

Area of Science:

• Dental Education
• Artificial Intelligence in Medicine
• Endodontics

Background:

• Artificial intelligence (AI) is transforming education, with AI chatbots showing potential in specialized fields like dentistry.
• Endodontic education faces knowledge gaps regarding AI chatbot capabilities and limitations, hindering effective adoption.
• Understanding AI chatbot functionalities (NLP, ML, DL) is crucial for their application in specialized learning environments.

Purpose of the Study:

• To explain AI chatbot functionalities, including natural language processing (NLP), machine learning (ML), and deep learning (DL).
• To explore AI chatbot applications in endodontic education for personalized learning, interactive training, and clinical decision support.
• To discuss challenges associated with AI chatbots, such as technical limitations, ethical concerns, and misinformation.

Main Methods:

• This narrative review synthesizes current literature on AI chatbots and their role in endodontic education.
• The review analyzes the core technologies underpinning AI chatbots.
• It examines reported benefits and challenges of AI chatbot integration in educational settings.

Main Results:

• AI chatbots provide immediate knowledge access, personalized learning experiences, and clinical reasoning development through case-based scenarios.
• Educators can benefit from streamlined curriculum development, automated assessments, and integrated evidence-based resources.
• Concerns include chatbot inaccuracies (hallucinations), algorithmic bias, plagiarism risks, and the potential for spreading misinformation.

Conclusions:

• AI chatbots offer significant potential to enhance endodontic education, but their effective integration requires addressing technical and ethical challenges.
• Limited endodontic-specific research necessitates the development and validation of tailored AI chatbot solutions.
• Collaborative efforts are essential for ethical implementation, accurate content, and robust evaluation frameworks for AI in endodontics.