{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:11:39Z","timestamp":1787670699641,"version":"build-2736575974"},"reference-count":32,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Investigation"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Importance<\/jats:title>\n                    <jats:p>Rigorous evaluation of large language models (LLMs) in pediatric diagnosis using authentic clinical presentations remains limited, particularly regarding response consistency and rare disease recognition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the diagnostic accuracy, consistency, and clinical usability of LLMs as diagnostic support tools in pediatric medicine compared with human clinicians using real\u2010world cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross\u2010sectional study at Sant Joan de D\u00e9u Barcelona Children's Hospital evaluated four LLMs [DxGPT\/GPT\u20104 (0613), Claude\u20103.5 Sonnet, GPT\u20104o (0513), and o1\u2010preview] against 78 pediatric clinicians using 50 real clinical cases (25 rare diseases, 25 common conditions) from a single tertiary pediatric center. All cases were presented using Spanish intake\u2010style clinical summaries. Each case was queried three times per LLM and evaluated by clinicians with different experience levels. Performance was assessed using the Top\u20101 and Top\u20105 diagnostic accuracy, response consistency (intraclass correlation coefficient), and qualitative evaluation. Extended clinical information was provided for 20 cases to assess the diagnostic efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Advanced LLMs significantly outperformed the clinicians in terms of diagnostic accuracy. o1\u2010preview and Claude\u20103.5 Sonnet achieved mean Top\u20101 accuracies of 60.0% and 59.0%, respectively, compared to clinicians\u2019 48.2% (odds ratios [ORs]: 2.99 and 2.75, both\n                      <jats:italic>P<\/jats:italic>\n                      &lt; 0.001). Performance advantages were most pronounced for rare diseases, where o1\u2010preview demonstrated 6\u2010fold higher Top\u20105 diagnostic odds compared to clinicians (ORs: 6.00,\n                      <jats:italic>P<\/jats:italic>\n                      &lt; 0.001). Extended clinical information improved the accuracy of both groups, particularly for rare diseases. Human\u2010Artificial intelligence complementarity analysis revealed 94.3% union accuracy with o1\u2010preview, representing a 10\u2010percentage\u2010point uplift over clinicians alone. Clinicians rated DxGPT favorably (mean, 3.9\/5), particularly for rare case support (4.1\/5).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>In this proof\u2010of\u2010concept study of a reference care center, newer LLMs outperformed previous models and human clinicians in complex pediatric diagnostics, particularly for rare diseases. These findings support further evaluation as augmentative diagnostic tools in similar settings, with appropriate legal, ethical, and clinical oversight frameworks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ped4.70053","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:20:25Z","timestamp":1774448425000},"page":"258-268","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Large\u2010language\u2010models for pediatric diagnosis: Performance evaluation using real\u2010world clinical notes from common and rare cases"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0913-9303","authenticated-orcid":false,"given":"Cristian","family":"Launes","sequence":"first","affiliation":[{"name":"Digital Strategy and Data Department Hospital Sant Joan de D\u00e9u  Barcelona Spain"},{"name":"Pediatrics Department Hospital Sant Joan de D\u00e9u  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