{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T15:31:11Z","timestamp":1762183871088,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In this study, we present LizAI XT, an AI-powered platform designed to automate the structuring, anonymization, and semantic integration of large-scale healthcare data from diverse sources, into one comprehensive table or any designated forms, based on diseases, clinical variables, and\/or other defined parameters, beyond the creation of a dashboard or visualization. We evaluate the platform\u2019s performance on a cluster of 4x NVIDIA A30 GPU 24GB, with 16 diseases\u2014from deathly cancer and COPD, to conventional ones\u2014ear infections, including a total 16,000 patients, \u223c115,000 medical files, and \u223c800 clinical variables. LizAI XT structures data from thousands of files into sets of variables for each disease in one file, achieving &gt; 95.0% overall accuracy, while providing exceptional outputs in complicated cases of cancers (99.1%), COPD (98.89%), and asthma (98.12%), without model-overfitting. Data retrieval is sub-second for a variable per patient with a minimal GPU power, which can significantly be improved on more powerful GPUs. LizAI XT uniquely enables fully client-controlled data, complying with strict data security and privacy regulations per region\/nation. Our advances complement the existing EMR\/EHR, AWS HealthLake, and Google Vertex AI platforms, for healthcare data management and AI development, with large-scalability and expansion at any levels of HMOs, clinics, pharma, and government.<\/jats:p>","DOI":"10.3390\/bdcc9110275","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:55:22Z","timestamp":1762178122000},"page":"275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LizAI XT\u2014AI-Accelerated Management Platform for Complex Healthcare Data at Scale, Beyond EMR\/EHR and Dashboards"],"prefix":"10.3390","volume":"9","author":[{"given":"Trung Tin","family":"Nguyen","sequence":"first","affiliation":[{"name":"LizAI Inc., Newton, MA 02459, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0169-4853","authenticated-orcid":false,"given":"David Raphael","family":"Elmaleh","sequence":"additional","affiliation":[{"name":"LizAI Inc., Newton, MA 02459, USA"},{"name":"Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","unstructured":"OpenAI (2025, January 15). 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