{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T15:07:30Z","timestamp":1792595250187,"version":"4.5.0"},"posted":{"date-parts":[[2025,6,7]]},"group-title":"Health Informatics","reference-count":83,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,6,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Artificial intelligence (AI) is rapidly transforming healthcare, but its benefits are not reaching all patients equally. Children remain overlooked with only 17% of FDA-approved medical AI devices labeled for pediatric use. In this work, we demonstrate that this exclusion may stem from a fundamental data gap. Our systematic review of 181 public medical imaging datasets reveals that children represent just under 1% of available data, while the majority of machine learning imaging conference papers we surveyed utilized publicly available data for methods development. Much like systematic biases of other kinds in model development, past studies have demonstrated the manner in which pediatric representation in data used for models intended for the pediatric population is essential for model performance in that population. We add to these findings, showing that adult-trained chest radiograph models exhibit significant age bias when applied to pediatric populations, with higher false positive rates in younger children. This work underscores the urgent need for increased pediatric representation in publicly accessible medical datasets. We provide actionable recommendations for researchers, policymakers, and data curators to address this age equity gap and ensure AI benefits patients of all ages.<\/jats:p>\n                <jats:sec>\n                  <jats:title>1-2 sentence summary<\/jats:title>\n                  <jats:p>Our analysis reveals a critical healthcare age disparity: children represent less than 1% of public medical imaging datasets. This gap in representation leads to biased predictions across medical image foundation models, with the youngest patients facing the highest risk of misdiagnosis.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.06.06.25328913","type":"posted-content","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T21:10:15Z","timestamp":1749330615000},"source":"Crossref","is-referenced-by-count":24,"title":["Lack of children in public medical imaging data points to growing age bias in biomedical AI"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2291-1572","authenticated-orcid":false,"given":"Stanley Bryan Zamora","family":"Hua","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8516-8707","authenticated-orcid":false,"given":"Nicholas","family":"Heller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1729-5071","authenticated-orcid":false,"given":"Alexander J.","family":"Towbin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0173-9133","authenticated-orcid":false,"given":"Irene Y.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9568-3155","authenticated-orcid":false,"given":"Alex X.","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7106-2669","authenticated-orcid":false,"given":"Lauren","family":"Erdman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2025061204501038000_2025.06.06.25328913v2.1","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-023-04698-z"},{"key":"2025061204501038000_2025.06.06.25328913v2.2","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11040337"},{"key":"2025061204501038000_2025.06.06.25328913v2.3","doi-asserted-by":"crossref","unstructured":"Johnson AEW , Pollard TJ , Shen L , et al. 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