{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:07:35Z","timestamp":1788934055280,"version":"build-2803163510"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Parents and caregivers increasingly use generative artificial intelligence chatbots for child health information. Pediatric vitamin D deficiency is a clinically relevant topic because advice about supplementation, testing, rickets, high-risk children, toxicity, and emergency symptoms can influence caregiver decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To compare the safety, medical accuracy, empathy, information reliability, educational quality, transparency, global quality, and readability of five large language model-driven chatbots when answering questions about pediatric vitamin D deficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This cross-sectional comparative study was reported with reference to CHART. An expert-curated 44-question set was selected from a 92-question candidate pool informed by search trends, caregiver-facing sources, clinical guidelines, and expert discussion. Each of the 44 questions was submitted once to each of five chatbot services\u2014ChatGPT-5.5, Gemini 3.1 Pro, Qianwen 3.6-Plus, DeepSeek V4, and Doubao-Seed-2.0 Pro\u2014using the same parent-oriented instruction. Responses were assessed for safety, accuracy, empathy, DISCERN, EQIP, JAMA criteria, GQS, and readability. Paired question-level differences were analyzed using Friedman tests, Kendall's\n                      <jats:italic>W<\/jats:italic>\n                      , and Cochran's\n                      <jats:italic>Q<\/jats:italic>\n                      ; results were interpreted as a single-run, time-specific snapshot.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Inter-rater agreement was good to excellent (Fleiss' kappa\u2009=\u20090.842 for safety; ICCs 0.846\u20130.914 for other subjective metrics). Twenty of 220 responses (9.1%) were unsafe; safety did not differ significantly across models (Cochran's\n                      <jats:italic>Q<\/jats:italic>\n                      \u2009=\u20093.704,\n                      <jats:italic>df<\/jats:italic>\n                      \u2009=\u20094,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.448). Significant inter-model differences were observed in accuracy, empathy, DISCERN, EQIP, JAMA, GQS, and all readability indices. ChatGPT had the highest median accuracy [5.00 (4.00, 5.00)] and DISCERN score [69.60 (65.25, 72.40)]; DeepSeek and Doubao had the highest empathy scores [5.00 (4.80, 5.00)]. ChatGPT and Doubao shared the highest EQIP median (86.00), Doubao had the highest GQS [5.00 (4.00, 5.00)], and Gemini had the highest FRES. JAMA scores were low across models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this single-run evaluation, the five chatbot services showed domain-specific performance differences without a significant safety difference. Findings are exploratory rather than evidence of stable model superiority and support multidimensional evaluation with clinician involvement for high-risk or individualized pediatric advice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1927624","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T05:57:13Z","timestamp":1788933433000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-sectional comparative evaluation of five large language model\u2013driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency"],"prefix":"10.3389","volume":"14","author":[{"given":"Ping","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Clinical Nutrition, Children\u2019s Hospital of Soochow University","place":["Suzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition, Children\u2019s Hospital of Soochow University","place":["Suzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition, Children\u2019s Hospital of Soochow University","place":["Suzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baicheng","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition, Children\u2019s Hospital of Soochow University","place":["Suzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition, Children\u2019s Hospital of Soochow University","place":["Suzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition, Children\u2019s Hospital of Soochow University","place":["Suzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1016\/S0140-6736(13)61650-5","article-title":"Rickets","volume":"383","author":"Elder","year":"2014","journal-title":"Lancet"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1016\/S0140-6736(03)14636-3","article-title":"Rickets","volume":"362","author":"Wharton","year":"2003","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1210\/jc.2015-2175","article-title":"Global consensus recommendations on prevention and management of nutritional rickets","volume":"101","author":"Munns","year":"2016","journal-title":"J Clin Endocrinol Metab"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1542\/peds.2008-1862","article-title":"American Academy of pediatrics section on breastfeeding; American academy of pediatrics committee on nutrition. 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