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Current brain injury assessment approaches cannot simultaneously meet the requirements of efficiency and interpretability in healthcare with high\u2010risk diagnoses and treatment. Meanwhile, limited and expensive biomechanics data pose significant challenges in AI training. Herein, feature\u2010based Gaussian process emulators are proposed to enable interpretable data\u2010driven modeling with limited biomechanics data under noise. Furthermore, a reliable AI platform, BrainGuard is developed, for efficiently providing a reliable and quantitative patient\u2010specific basis and real\u2010time monitoring of BCI caused brain injury. These results demonstrate exceptional performance of BrainGuard in rapidly and accurately predicting and monitoring the full\u2010field von Mises strain revealing the brain injury even under challenging noise conditions. By constructing interpretable digital brain twins to offer reliable digital healthcare solutions, the platform enhances real\u2010time patient protection and improves the security and durability of long\u2010term BCI\u2010based measurement and treatment strategies.<\/jats:p>","DOI":"10.1002\/advs.202506747","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T15:39:35Z","timestamp":1766590775000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reliable AI Platform for Monitoring BCI Caused Brain Injury and Providing Real\u2010Time Protection"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7911-8885","authenticated-orcid":false,"given":"Chufan","family":"He","sequence":"first","affiliation":[{"name":"School of Mechanics and Engineering Science Peking University  Beijing 100871 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjun","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Forensic Medicine, Xiangya School of Basic Medical Sciences Central South\u00a0University Changsha 410013 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timon","family":"Rabczuk","sequence":"additional","affiliation":[{"name":"Institute of Structural Mechanics Bauhaus University Weimar  99423 Weimar Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5086-960X","authenticated-orcid":false,"given":"Chensen","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Mechanics and Engineering Science Peking University  Beijing 100871 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"e_1_2_8_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-022-00913-9"},{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1403112111"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2014.252"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-019-0234-8"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13311-022-01190-2"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature24636"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06839-2"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aav2842"},{"key":"e_1_2_8_9_1","first-page":"627","volume":"19","author":"Drew L.","year":"2024","journal-title":"Nature"},{"key":"e_1_2_8_10_1","volume":"21","author":"Musk E.","year":"2019","journal-title":"J. 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