[Artificial intelligence-driven early warning for severe pneumonia: pathways and challenges from data integration to clinical decision-making].

Abstract

Severe pneumonia, characterized by rapid progression and high heterogeneity, is a leading cause of patient mortality. Early identification of high-risk patients is crucial for improving outcomes. Traditional risk stratification tools, such as CURB-65 score and pneumonia severity index (PSI), primarily rely on static variables, which inadequately capture the dynamic evolution of the disease and offer limited capabilities for individualization. In recent years, artificial intelligence (AI) technologies, particularly machine learning, deep learning, and multimodal fusion models, have demonstrated significant potential for enhancing early warning capabilities for severe pneumonia. Starting from clinical challenges and integrating recent domestic and international advances, this review focuses on the current status of AI applications and key technological pathways in the early warning of severe pneumonia. By incorporating the foundational work and practical experiences of representative research teams in China, it further discusses future development directions in this field. AI-driven early warning systems are facilitating a shift in severe pneumonia management from passive response to proactive intervention. However, their clinical translation faces challenges, including insufficient generalizability, limited interpretability, and a lack of prospective validation. Future progress requires fostering multicenter collaboration, promoting data standardization, and integrating mechanistic studies to effectively embed AI into the clinical decision-making ecosystem.