To explore the focus and trends in real-world studies in Chinese through knowledge mapping method, databases CNKI, VIP, Wanfang and Sinomed were retrieved, with 1 757 relevant articles published before September 30rd, 2020 finally included, whose bibliographical records were imported into NoteExpress to avoid duplication and check relativity. VOSviewer, a bibliometric analysis tool, was used to analyze their development. It was found that real-world studies have mainly taken shape after 2010, in which traditional Chinese medicine research plays an important role. China Journal of Chinese Material Medica was the leading journal with 120 papers, the China Academy of Chinese Medical Sciences the most contribution institution with 338 papers, and Xie Yanming from the institution the most contribution author with 250 papers. This study helps clinicians and researchers in better understanding the evolution of real-world research over more than two decades in China.
Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.