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

Tatsuya Akitomo¹, Masakazu Hamada², Shuma Hamaguchi¹

¹Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

International Dental Journal

|January 29, 2026

Summary

Large language models (LLMs) show promise in pediatric dentistry for professionals and patients, but accuracy concerns mean they should be used as supplementary tools only. Further research is needed to enhance their reliability in dental care.

Keywords:

Artificial intelligence Diagnostic assistance Large language models Paediatric dentistry Patient education

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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.