Evaluating the efficacy of large language models in providing information for parental inquiries regarding primary care of pediatric oral and dental health

Background: The use of artificial intelligence (AI)-based large language models (LLMs) for accessing health information is rapidly increasing; however, limited research has evaluated their efficacy in pediatric dentistry, particularly regarding the accuracy of oral health information. This study aimed to assess the accuracy, readability, and similarity of four AI-based LLMs; ChatGPT 4.0, Google Gemini, Microsoft Copilot, and DeepSeek-R1 in responding to parental questions about children’s oral and dental health. Methods: Twenty frequently asked questions, developed by experienced pediatric dentists, were presented to each LLM over a seven-day period, from 27 January 2025 to 03 February 2025. Responses were independently assessed for accuracy, readability, and similarity. Statistical analyses used IBM SPSS 27.0. Normality was checked by Kolmogorov-Smirnov. T-tests/Analysis of Variance (ANOVA) with Tukey’s Honestly Significant Difference (HSD) were applied for normal data; Mann-Whitney U/Kruskal-Wallis with Dunn’s post hoc and Bonferroni correction for non-normal data. Fisher’s Exact test examined categorical variables; binary accuracy after Likert dichotomization. Inter/intra-rater reliability employed two-way random effects Intraclass Correlation Coefficients (ICC). p < 0.05 indicated significance. Results: The study demonstrated that ChatGPT 4.0 and DeepSeek-R1 achieved significantly higher accuracy scores compared to Google Gemini and Microsoft Copilot (p < 0.001). ChatGPT 4.0 also produced the most readable content, reflected by the lowest Average Reading Level Consensus (ARLC) score of 8.05, whereas DeepSeek-R1 generated the shortest responses (p < 0.001). Regarding originality, ChatGPT 4.0 and Google Gemini exhibited the lowest similarity indices, indicating a greater degree of response diversity (p = 0.02). Conclusions: These findings underscore the potential role of AI-based LLMs in facilitating parental access to evidence-based pediatric dental health information. Based on accuracy, readability, and similarity results, ChatGPT 4.0 appears the most reliable platform for delivering oral health information to parents. Furthermore, the newly introduced DeepSeek-R1 showed comparable accuracy and originality to ChatGPT 4.0, highlighting its promise as an efficient tool for user-friendly dental health guidance.

Large language models; Artificial intelligence; Oral health; Pediatric dentistry

[1] Wang M, Zhang Y, Li X, Liu X. Understanding and reducing delayed dental care for early childhood caries: a structural equation model approach. BMC Public Health. 2025; 25: 523.

[2] Giles E, Gray-Burrows KA, Bhatti A, Rutter L, Purdy J, Zoltie T, et al. “Strong Teeth”: an early-phase study to assess the feasibility of an oral health intervention delivered by dental teams to parents of young children. BMC Oral Health. 2021; 21: 267.

[3] Chauhan A, Staples A, Forshaw E, Zoltie T, Nasser R, Gray-Burrows KA, et al. Exploring and enhancing the accessibility of children’s oral health resources (called HABIT) for high risk communities. Frontiers in Oral Health. 2024; 5: 1392388.

[4] Abdelrahman HH, Hamza M, Essam W, Adham M, AbdulKafi A, Baniode M. Electronic oral health surveillance system for Egyptian preschoolers using District Health Information System (DHIS2): design description and time motion study. BMC Oral Health. 2024; 24: 807.

[5] Cooper D, Kim J, Duderstadt K, Stewart R, Lin B, Alkon A. Interprofessional oral health education improves knowledge, confidence, and practice for pediatric healthcare providers. Frontiers in Public Health. 2017; 5: 209.

[6] Gugnani N, Pandit IK, Gupta M, Gugnani S, Kathuria S. Parental concerns about oral health of children: is ChatGPT helpful in finding appropriate answers? Journal of Indian Society of Pedodontics and Preventive Dentistry. 2024; 42: 104–111.

[7] Avanzo M, Stancanello J, Pirrone G, Drigo A, Retico A. The evolution of artificial intelligence in medical imaging: from computer science to machine and deep learning. Cancers. 2024; 16: 3702.

[8] Pezoulas VC, Zaridis DI, Mylona E, Androutsos C, Apostolidis K, Tachos NS, et al. Synthetic data generation methods in healthcare: a review on open-source tools and methods. Computational and Structural Biotechnology Journal. 2024; 23: 2892–2910.

[9] Dermata A, Arhakis A, Makrygiannakis MA, Giannakopoulos K, Kaklamanos EG. Evaluating the evidence-based potential of six large language models in paediatric dentistry: a comparative study on generative artificial intelligence. European Archives of Paediatric Dentistry. 2025; 26: 527–535.

[10] Zhu J, Chen Z, Zhao J, Yu Y, Li X, Shi K, et al. Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study. BMC Oral Health. 2023; 23: 358.

[11] Zhang B, Campbell J. Beyond passive learning: artificial intelligence–driven spaced repetition learning and digital tools in oral and maxillofacial surgery residency. Journal of Oral and Maxillofacial Surgery. 2025; 83: 1060–1064.

[12] Guo X, Shao Y. AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments—a narrative review. Frontiers in Dental Medicine. 2025; 6: 1612441.

[13] Mustuloğlu Ş, Deniz BP. Evaluation of chatbots in the emergency management of avulsion injuries. Dental Traumatology. 2025; 41: 437–444.

[14] Guven Y, Ozdemir OT, Kavan MY. Performance of artificial intelligence chatbots in responding to patient queries related to traumatic dental injuries: a comparative study. Dental Traumatology. 2025; 41: 338–347.

[15] Rokhshad R, Fadul M, Zhai G, Carr K, Jackson JG, Zhang P. A comparative analysis of responses of artificial intelligence chatbots in special needs dentistry. Pediatric Dentistry. 2024; 46: 337–344.

[16] Öztürk Z, Bal C, Çelikkaya BN. Evaluation of information provided by ChatGPT versions on traumatic dental injuries for dental students and professionals. Dental Traumatology. 2025; 41: 427–436.

[17] American Academy of Pediatric Dentistry. Policy on the role of dental prophylaxis in pediatric dentistry. The Reference Manual of Pediatric Dentistry (pp. 95–97). American Academy of Pediatric Dentistry: Chicago, Ill. 2024.

[18] American Academy of Pediatric Dentistry. Policy on use of fluoride. The Reference Manual of Pediatric Dentistry (pp. 101–103). American Academy of Pediatric Dentistry: Chicago, Ill. 2024.

[19] American Academy of Pediatric Dentistry. Policy on dietary recommendations for infants, children, and adolescents. The Reference Manual of Pediatric Dentistry (pp. 109–113). American Academy of Pediatric Dentistry: Chicago, Ill. 2024.

[20] American Academy of Pediatric Dentistry. Periodicity of examination, preventive dental services, anticipatory guidance/counseling, and oral treatment for infants, children, and adolescents. The Reference Manual of Pediatric Dentistry (pp. 293–305). American Academy of Pediatric Dentistry: Chicago, Ill. 2024.

[21] American Academy of Pediatric Dentistry. Fluoride therapy. The Reference Manual of Pediatric Dentistry (pp. 351–357). American Academy of Pediatric Dentistry: Chicago, Ill. 2024.

[22] Malak A, Şahin MF. How useful are current chatbots regarding urology patient information? Comparison of the ten most popular chatbots’ responses about female urinary incontinence. Journal of Medical Systems. 2024; 48: 102.

[23] Molena KF, Macedo AP, Ijaz A, Carvalho FK, Gallo MJD, Wanderley Garcia de Paula e Silva F, et al. Assessing the accuracy, completeness, and reliability of artificial intelligence-generated responses in dentistry: a pilot study evaluating the ChatGPT model. Cureus. 2024; 16: e65658.

[24] Kusaka S, Akitomo T, Hamada M, Asao Y, Iwamoto Y, Tachikake M, et al. Usefulness of generative artificial intelligence (AI) tools in pediatric dentistry. Diagnostics. 2024; 14: 2818.

[25] Rokhshad R, Zhang P, Mohammad-Rahimi H, Pitchika V, Entezari N, Schwendicke F. Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: a pilot study. Journal of Dentistry. 2024; 144: 104938.

[26] Bhatara S, Goswami M, Saxena A, Pathak P, Tuli S, Saxena B. The evolving role of social media in paediatric dentistry: a narrative review. Global Pediatrics. 2024; 9: 100221.