Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Introduction: Artificial intelligence (AI) tools such as ChatGPT, Google Gemini, and Microsoft Copilot are increasingly relied upon by parents for immediate guidance on pediatric dental concerns. This study evaluated and compared the response quality of these AI platforms in addressing real-world parental queries related to pediatric dentistry, including early tooth extraction, space maintenance, and the decision to consult a pediatric or a general dentist.

Methods: A structured 30-question survey was developed and submitted to each AI model, and their responses were anonymized and assessed by pediatric dental experts using a standardized rubric across five key domains: clinical accuracy, clarity, completeness, relevance, and absence of misleading information.

Results: Statistically significant differences were found across all five domains ( < .001), with ChatGPT consistently achieving the highest scores. Multivariate analysis (MANOVA) confirmed a strong overall effect of the AI model on response quality (Pillai's Trace = 0.892,  < .001), supporting ChatGPT's superior performance in providing accurate, relevant, and comprehensive pediatric dental advice.

Discussion: While AI technologies show potential as clinical decision support systems, their variable performance reinforces the need for expert oversight. Future AI development should focus on optimizing response quality and safety to ensure effective and trustworthy digital health communication for pediatric dental care.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12394529PMC
http://dx.doi.org/10.3389/froh.2025.1652422DOI Listing

Publication Analysis

Top Keywords

pediatric dental
12
pediatric
5
brush byte
4
byte bot
4
bot quality
4
quality comparison
4
comparison artificial
4
artificial intelligence-generated
4
intelligence-generated pediatric
4
dental advice
4

Similar Publications

Purpose: Uganda faces significant oral health workforce shortages, limiting access to dental care. The Uganda Christian University School of Dentistry (UCUSoD) implemented a blended learning approach to enhance dental education by integrating online training. This qualitative study assesses the feasibility and effectiveness of hybrid learning in improving students' knowledge and preparedness for clinical practice.

View Article and Find Full Text PDF

Key Mediators Reducing Socioeconomic Inequality in Early Childhood Caries.

JDR Clin Trans Res

September 2025

School of Dentistry, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, Queensland, Australia.

Objectives: Socioeconomic status (SES) has a significant effect on the burden of early childhood caries (ECC), yet addressing SES disparities remains challenging. This study aimed to identify and quantify the most impactful mediator linking SES effect to the occurrence of ECC using advanced causal mediation analysis, to inform targeted interventions that reduce SES-related disparities in ECC.

Methods: Data were drawn from the Study of Mothers' and Infants' Life Events, a cohort of 2,182 mother-child dyads recruited from Adelaide's 3 largest public hospitals (2013-2014).

View Article and Find Full Text PDF

Can ChatGPT-4.5 Accurately Identify Teeth? A Cross-Sectional Comparison With Dental Students and Parents.

Int J Paediatr Dent

September 2025

Lokman Hekim University, Faculty of Dentistry, Department of Pediatric Dentistry, Ankara, Turkey.

Background: Differentiating between primary and permanent teeth is a critical component of oral health knowledge, influencing both preventive care and clinical decisions. With the growing use of artificial intelligence (AI) in healthcare and education, its role in supporting learning is of increasing interest.

Aim: This study evaluated the diagnostic accuracy and internal consistency of ChatGPT-4.

View Article and Find Full Text PDF

LONP1 Variants Are Associated With Clinically Diverse Phenotypes.

Clin Genet

September 2025

Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

LONP1 encodes a mitochondrial protease essential for protein quality control and metabolism. Variants in LONP1 are associated with a diverse and expanding spectrum of disorders, including Cerebral, Ocular, Dental, Auricular, and Skeletal anomalies syndrome (CODAS), congenital diaphragmatic hernia (CDH), and neurodevelopmental disorders (NDD), with some individuals exhibiting features of mitochondrial encephalopathy. We report 16 novel LONP1 variants identified in 16 individuals (11 with NDD, 5 with CDH), further expanding the clinical spectrum.

View Article and Find Full Text PDF