摘要Pulmonary function test(PFT)is a vital noninvasive method for evaluating respiratory system function and is widely used in the diagnosis,surgical risk assessment,and prognostic prediction of chronic airway diseases and interstitial lung diseases.In 2021,the American Thoracic Society and the European Respiratory Society updated their pulmonary function interpretation guidelines,choosing to adopt z-scores instead of percentage of predicted forced expiratory volume in 1 second(FEV1),thereby significantly improving sensitivity and prognostic eva-luation.However,conventional PFT relies on patient cooperation and is limited in its ability to effectively capture the heterogeneity of localized lesions.In recent years,the use of multimodal medical imaging in pul-monary function prediction has progressed rapidly.When combined with radiomics and deep learning techni-ques,these imaging methods enable more accurate and quantitative functional assessments.This review in-troduces that pulmonary function assessment should integrate multimodal imaging features with clinical functional indicators.This approach establishes cross-scale linkages between morphology and function and provides new perspectives for the early screening of high-risk individuals with accelerated pulmonary aging,evaluating the effectiveness of interventions,and enabling personalized intervention strategies.We review the classification systems and clinical value of PFT,discuss the advances and limitations of multimodal imaging in predicting lung function,and explore future directions in standardized data acquisition and multicenter vali-dation to promote precise pulmonary function assessment and clinical application.