Generative Artificial Intelligence and Large Language Models in Paediatric Dentistry: A Scoping Review.

Area of Science:

• Dentistry
• Artificial Intelligence
• Natural Language Processing

Background:

• Artificial intelligence (AI), specifically large language models (LLMs), is increasingly used in dentistry.
• Existing reviews on AI in pediatric dentistry primarily focus on diagnostic assistance.
• No comprehensive reviews have addressed LLM applications for all users, including patients and guardians.

Purpose of the Study:

• To review the current applications of large language models (LLMs) in pediatric dentistry.
• To assess the scope of LLM use across different user groups (professionals, students, patients, guardians).
• To evaluate the potential and limitations of LLMs in pediatric dental care.

Main Methods:

• A systematic literature search was conducted in PubMed, Scopus, and Web of Science in September 2025.
• Search terms included "Pediatric dentistry" combined with specific LLM names (ChatGPT, Gemini, Claude, Copilot, DeepSeek).
• Eligibility criteria were applied to 262 identified articles, with 30 included in the final review.

Main Results:

• The majority of included studies (24/30) were published in 2025.
• The primary LLM application identified was "answers to questions" (18 articles), followed by "diagnostic assistance" (8 articles) and "dental examination" (3 articles).
• LLMs show potential for diverse users in pediatric dentistry, including students, professionals, patients, and guardians.

Conclusions:

• Large language models (LLMs) offer promising applications in pediatric dentistry for various stakeholders.
• Current LLMs may provide inaccurate information, necessitating their use as supplementary tools.
• Appropriate application and continued research are crucial for expanding the reliable use of LLMs in pediatric dental settings.