The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

Objective:To explore the application value of diaphragmatic thickening fraction (DTF) and lung ultrasound score (LUS) in predicting the weaning outcome of elderly patients with acute respiratory distress syndrome (ARDS) under mechanical ventilation, and to analyze their correlation, thereby providing evidence for clinical decision-making.Methods:A retrospective analysis was conducted on elderly ARDS patients admitted to the ICU of the First Affiliated Hospital of Jinzhou Medical University from January 2020 to December 2023. The inclusion criteria included age > 60 years, endotracheal intubation, mechanical ventilation time >24 h, and a diagnosis of ARDS based on the Berlin definition. Exclusion criteria included neuromuscular diseases, spinal cord injury, post-thoracoabdominal surgery, thoracic or tracheal deformity, and mid-course tracheostomy conversion. Patients were divided into a success group and a failure group based on weaning outcomes. Demographic data, Acute Physiology and Chronic Health EvaluationⅡ (APACHEⅡ) scores, Sequential Organ Failure Assessment (SOFA) scores, oxygenation index at ICU admission, and pre-extubation DTF, LUS, and oxygenation index were recorded. Binary logistic regression analysis was used to identify independent risk factors affecting weaning outcomes. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of DTF and LUS for weaning outcomes. Pearson correlation analysis was conducted to examine the relationship between DTF and LUS.Results:A total of 317 patients were included, including 212 in the success group and 105 in the failure group. There were no statistically significant differences in gender, age, APACHEⅡ score, SOFA score, etc., between the two groups (all P>0.05). Pre-weaning LUS was higher in the failure group than in the success group [(17.26±3.04) vs. (13.69±4.06), P<0.001], and the DTF was significantly lower than that of the successful group [(27.83%±6.37%) vs. (40.15%±6.49%), P<0.001]. Binary logistic regression identified LUS and DTF as independent influencing factors for weaning outcomes (both P<0.05). ROC analysis revealed that LUS predicted weaning failure with an AUC of 0.748 (95% CI: 0.695-0.801, P<0.001), sensitivity of 83.81% and specificity of 56.60%. DTF predicted weaning success with an AUC of 0.935 (95% CI: 0.909-0.961, P<0.001), sensitivity of 83.02% and specificity of 89.52%. A negative correlation was observed between LUS and DTF before weaning ( r=-0.385, P<0.001). Conclusions:Both DTF and LUS are effective indicators for assessing weaning outcomes in elderly ARDS patients, offering complementary clinical insights. Higher LUS reflects more severe pulmonary pathology and increased weaning risk, while lower DTF indicates impaired diaphragmatic function and reduced likelihood of successful extubation. Integration of these parameters provides a comprehensive foundation for clinical decision-making.