In recent years, lung function assessment has attracted increasing attention in the perioperative management of thoracic surgery. However, traditional pulmonary function testing methods remain limited in clinical practice due to high equipment requirements and complex procedures. With the rapid development of artificial intelligence (AI) technology, lung function assessment based on multimodal chest imaging (such as X-rays, CT, and MRI) has become a new research focus. Through deep learning algorithms, AI models can accurately extract imaging features of patients and have made significant progress in quantitative analysis of pulmonary ventilation, evaluation of diffusion capacity, measurement of lung volumes, and prediction of lung function decline. Previous studies have demonstrated that AI models perform well in predicting key indicators such as forced expiratory volume in one second, diffusing capacity for carbon monoxide, and total lung capacity. Despite these promising prospects, challenges remain in clinical translation, including insufficient data standardization, limited model interpretability, and the lack of prediction models for postoperative complications. In the future, greater emphasis should be placed on multicenter collaboration, the construction of high-quality databases, the promotion of multimodal data integration, and clinical validation to further enhance the application value of AI technology in precision decision-making for thoracic surgery.

In recent years, lung function assessment has attracted increasing attention in the perioperative management of thoracic surgery. However, traditional pulmonary function testing methods remain limited in clinical practice due to high equipment requirements and complex procedures. With the rapid development of artificial intelligence (AI) technology, lung function assessment based on multimodal chest imaging (such as X-rays, CT, and MRI) has become a new research focus. Through deep learning algorithms, AI models can accurately extract imaging features of patients and have made significant progress in quantitative analysis of pulmonary ventilation, evaluation of diffusion capacity, measurement of lung volumes, and prediction of lung function decline. Previous studies have demonstrated that AI models perform well in predicting key indicators such as forced expiratory volume in one second, diffusing capacity for carbon monoxide, and total lung capacity. Despite these promising prospects, challenges remain in clinical translation, including insufficient data standardization, limited model interpretability, and the lack of prediction models for postoperative complications. In the future, greater emphasis should be placed on multicenter collaboration, the construction of high-quality databases, the promotion of multimodal data integration, and clinical validation to further enhance the application value of AI technology in precision decision-making for thoracic surgery.

Humans ; Neoplasms, Second Primary/epidemiology* ; Neoplasms/epidemiology* ; Risk Factors ; Prognosis

Based on the Nanshan class of clinical medicine in Guangzhou Medical University, the center established an interest-oriented thoracic surgery learning collaboration group. All recruited students received the full-range cultivation, including "MDT" learning collaboration with characteristics of early clinical practice, early scientific research, English training and thoracic surgery collaboration group as thecore, accelerated clinical skills training under the naked-eye 3D thoracoscopic system, and scientific research thinking which recommended by new media — "WeChat public platform". A total of 10 undergraduate students from 2013 to 2017 batch were recruited into the this group and they showed advantages in clinical technique, scientific research and higher education enrollment after cultivation, demonstrating that part of the clinical teaching and scientific research thinking of thoracic surgery gave to undergraduates in advance can pave the way for the training of thoracic surgeons and formulate more detailed and individualized programs to teach students in accordance with their aptitude in the future.