2025/05/15 by Trung Tin Nguyen, Nguyen, Trung Tin, Salomon M. Stemmer +3
Computer Science · Health Professions · Medicine · #Artificial Intelligence in Healthcare #Artificial Intelligence in Healthcare and Education #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2505.10592
openalex publication_date 2025/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
AI-powered LizAI XT ensures real-time and accurate mega-structure of different clinical datasets and largely inaccessible and fragmented sources, into one comprehensive table or any designated forms, based on diseases, clinical variables, and/or other defined parameters. We evaluate the platform's performance on a cluster of 4x NVIDIA A30 GPU 24GB, with 16 diseases -- from deathly cancer and COPD, to conventional ones -- ear infections, including a total 16,000 patients, ∼115,000 medical files, and ∼800 clinical variables. LizAI XT structures data from thousands of files into sets of variables for each disease in one file, achieving >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.