{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T06:11:34Z","timestamp":1777961494655,"version":"3.51.4"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Reducing under-5 mortality remains a global health priority. Large language models (LLMs) are increasingly used by the public to access medical information. However, current evidence evaluating LLMs\u2019 performance in public-facing child health communication is scarce.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We selected the top five search terms related to each of the five leading causes of under-5 mortality (prematurity, pneumonia, birth asphyxia, malaria, and diarrhoea) using Google Trends, generating 25 representative public queries. Responses were collected from four LLMs (ChatGPT-4.0, Claude 3.5 Sonnet, Bing AI, and Gemini) and independently evaluated by four pediatricians. We used the DISCERN instrument for information reliability; 5-point Likert scales for accuracy, completeness, and comprehensibility; Flesch Reading Ease (FRE) and Flesch\u2013Kincaid Grade Level (FKGL) indices for readability; and the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) for understandability and actionability. Differences among models were evaluated with Kruskal\u2013Wallis and ANOVA tests, with statistical significance set at\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We found significant performance variations among the four models across most evaluation metrics. Bing AI achieved the highest total DISCERN score (median 42) and the highest reliability subscore (Section A median 28). Claude consistently underperformed across multiple domains. Notably, readability was poor for all models, with high language complexity (mean FKGL score 12.4). Critically, actionability scores were near zero for all models on the PEMAT-P scale, reflecting a universal lack of clear and practical behavioral guidance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>While LLMs can generally provide accurate health information, limitations in readability and actionability restrict their practical application in public health communication. Future development should prioritize language simplification and clearer behavioral guidance to enhance their value in public-facing child health communication.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1646475","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:31:21Z","timestamp":1777959081000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Large language models and child mortality: opportunities and challenges in answering public queries on under-5 causes"],"prefix":"10.3389","volume":"14","author":[{"given":"Yi","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Pediatrics, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University","place":["Chengdu, China"]},{"name":"Department of Pediatric Otolaryngology Head and Neck Surgery, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingxi","family":"Zhu","sequence":"additional","affiliation":[{"name":"West China Biomedical Big Data Center, West China Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Med-X Center for Informatics, Sichuan University","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongju","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"West China Biomedical Big Data Center, West China Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanxia","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxi","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University","place":["Chengdu, China"]},{"name":"Children's Medicine Key Laboratory of Sichuan Province","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e195","DOI":"10.1016\/S2214-109X(21)00515-5","article-title":"Global, regional, and national trends in under-5 mortality between 1990 and 2019 with scenario-based projections until 2030: a systematic analysis by the UN inter-agency Group for Child Mortality 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