Evidence map›Paper›PMID 42287436›Full record

ArticleEuropean journal of pediatrics2026

Performance of artificial intelligence chatbots in providing feeding management and oral health guidance for children with cleft lip and palate: ChatGPT 5.2 vs Gemini 3 Pro.

Aslıhan Beşparmak, Tansu Merve Beşparmak

Abstract readComparative Study
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In one paragraph

Article in European journal of pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Aslıhan BeşparmakDepartment of Nutrition and Dietetics, Faculty of Health Science, Nuh Naci Yazgan University, Kayseri, Turkey.ORCID http://orcid.org/0000-0002-7262-3982
Tansu Merve BeşparmakDepartment of Endodontics, Faculty of Dentistry, Kirikkale University, Kirikkale, Turkey. tansumervebesparmak@gmail.com.ORCID http://orcid.org/0000-0001-5241-4932

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to comparatively evaluate the quality, reliability, and readability of responses generated by ChatGPT 5.2 and Gemini 3 Pro regarding feeding management and oral health guidance for children with cleft lip and palate (CLP). A cross-sectional design was used. Both models were asked 20 questions on feeding and oral-dental care in infants and children with CLP. Response quality was assessed using the Global Quality Score (GQS) and the CLEAR tool, reliability with the modified-DISCERN (m-DISCERN), and readability with the Flesch Reading Ease (FRES) and Flesch-Kincaid Grade Level (FKGL). The mean GQS was significantly higher for Gemini 3 Pro than for ChatGPT 5.2 (4.51 ± 0.29 vs. 3.76 ± 0.33; p < 0.001). The CLEAR tool score was likewise significantly higher in Gemini 3 Pro compared with ChatGPT 5.2 (22.95 ± 0.94 vs. 19.40 ± 1.47; p < 0.001), and the m-DISCERN scores were also significantly higher for Gemini 3 Pro (4.0 [4.0-4.0]) than for ChatGPT 5.2 (3.0 [2.0-3.0]; p < 0.001). The FRES values were significantly higher for Gemini 3 Pro compared with ChatGPT 5.2 (49.50 ± 10.33 vs. 23.60 ± 14.48; p < 0.001), whereas the FKGL values were significantly lower (9.77 ± 1.54 vs. 13.44 ± 2.81; p < 0.001). Correlation analysis revealed strong positive correlations between GQS and CLEAR (r = 0.753) and between GQS and m-DISCERN (r = 0.740), while FKGL showed significant negative correlations with all quality and reliability measures (-0.566≤ r ≤ -0.484; all p < 0.001).

conclusionGemini 3 Pro outperformed ChatGPT 5.2 across content quality, reliability, and readability. Although both models can support guidance in CLP care, AI chatbot outputs should be used as complementary tools alongside professional clinical guidance. WHAT IS KNOWN: • Large language model (LLM) chatbots are increasingly used as sources of health information for patients and families. • Existing studies on cleft lip and palate largely focus on single-model evaluations. WHAT IS NEW: • This study provides the first comparative analysis of ChatGPT 5.2 and Gemini 3 Pro for feeding management and oral-health guidance in CLP care. • Gemini 3 Pro demonstrated higher content quality, reliability, and readability than ChatGPT 5.2, indicating model-dependent variability in chatbot performance.

Indexed as

Artificial IntelligenceCleft LipCleft PalateOral HealthChildChild, PreschoolCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansInfantLarge Language ModelsMaleReproducibility of ResultsArtificial intelligenceChatbotsCleft lip and palateFeeding guidanceOral health

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.