Application prospects of multimodal models in the diagnosis and early warning of intra-abdominal infection

Intra-abdominal infection (IAI) is one of the most common and severe infectious diseases in general surgery, which is characterized by complex pathophysiology, high morbidity, and mortality, posing major challenges for clinical management. Conventional diagnostic approaches for IAI primarily rely on patients' clinical presentations, physical examinations, and auxiliary tests; however, these methods are often limited by delayed results, strong subjectivity, and fragmented information across modalities, which hinder early diagnosis and precise intervention. Multimodal artificial inte-lligence models offer a promising paradigm by integrating heterogeneous data sources from IAI patients, thereby overcoming the "data silo" problem and enabling more comprehensive disease assessment. The authors provide a detailed overview of the epidemiology, diagnostic status, and stratification challenges of IAI, summarize the recent progress of multimodal model in critical care medicine, and analyze the key issues of data fusion and standardization. Furthermore, They systema-tically discuss the diagnostic value of different data modalities in IAI and highlight advances in three pivotal technologies-temporal modeling, explainable artificial intelligence, and multimodal fusion algorithms. Finally, they outline the prospects of multimodal model in early warning, severity grading, and individualized treatment of IAI, as well as the real-world requirements for its clinical implemen-tation and resource allocation. The aim is to provide new insights for the precision management of IAI through AI-assisted multimodal modeling.