AI Large Model–Driven Methods for Cardiovascular and Cerebrovascular Disease Sample Analysis Oriented to Industrial Engineering

Abstract

Cardiovascular and cerebrovascular diseases remain leading causes of morbidity and mortality worldwide, posing significant challenges to healthcare systems in terms of diagnosis efficiency, resource allocation, and decision-making accuracy. With the rapid development of artificial intelligence, large-scale AI models have demonstrated remarkable capabilities in representation learning, multimodal data fusion, and complex pattern recognition. This study proposes an AI large model–driven framework for cardiovascular and cerebrovascular disease sample analysis, oriented toward industrial engineering principles. The proposed approach integrates clinical data, biomedical signals, imaging features, and laboratory indicators into a unified analytical pipeline powered by large AI models. By leveraging deep feature extraction and adaptive learning mechanisms, the framework enhances disease risk assessment, sample classification, and outcome prediction. From an industrial engineering perspective, the method emphasizes process optimization, system efficiency, and decision support, enabling scalable deployment in clinical workflows.