Abstract： Artificial intelligence (AI) has been applied increasingly in the medical field during the past 5 years. Within respiratory medicine, chest imaging AI is one of the relevant hotspots, commonly trained to identify pulmonary nodules/lung tumors, tuberculosis, pneumonia, interstitial lung disease, chronic obstructive pulmonary disease, pulmonary embolism and other pathologies. Due to the non-specific clinical manifestations and the low detection rate of pathogens, precise diagnosis and treatment of pneumonia remain challengeable. Since the outbreak of coronavirus disease 2019 (COVID-19), chest imaging AI has demonstrated its clinical value in accurate diagnosis and quantitative measurements of COVID-19. Moreover, an AI system can assist the clinicians to identify the high-risk COVID-19 patients who warrant close monitoring and timely intervention. However, there are still some limitations in the existing studies, such as small sample size, lack of multi-modal assessment of the AI model, and rough classification of pneumonia. Therefore, some suggestions for future research were put forward in this paper. Most of all, more attention should be paid to the collection of high-quality datasets, standardization of image annotation, technology innovation, algorithm optimization and model verification. Besides, the application of imaging AI on other types of pneumonia including viral pneumonia, bacterial pneumonia and pneumomycosis deserves further study. In conclusion, chest imaging AI is expected to play a vital role in decision-making for pneumonia in the future.