Objective To collect and store all interactions relating to medical information between our center and allied specialized hospitals by constructing a database system for thoracic surgery and pulmonary tuberculosis. Methods We collected all related medical records of patients who had been clinically diagnosed with pulmonary tuberculosis and tuberculous empyema using the CouchBase Database, including outpatient and inpatient system of the Department of Thoracic Surgery at the Public Health Clinical Center of Chengdu between January 2017 to June 2023. Then, we integrated all medical records derived from the radiology information system, hospital information system, image archiving and communication systems, and the laboratory information management system. Finally, we used artificial intelligence to generate a database system for the application of thoracic surgery on pulmonary tuberculosis, which stored structured medical data from different hospitals along with data collected from patients via WeChat users. The new database could share medical data between our center and allied hospitals by using a front-end processor. ResultsWe finally included 124 patients with 86 males and 38 females aged 43 (26, 56) years. A structured database for the application of thoracic surgery on patients with pulmonary tuberculosis was successfully constructed. A follow-up list created by the database can help outpatient doctors to complete follow-up tasks on time. All structured data can be downloaded in the form of Microsoft Excel files to meet the needs of different clinical researchers. Conclusion Our new database allows medical data to be structured, stored and shared between our center and allied hospitals. The database represents a powerful platform for interactions relating to regional information concerning pulmonary tuberculosis.
Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.