The goal of this observational study is to learn about the effectiveness of an artificial intelligence-based early warning system for predicting adverse events in patients with acute myocardial infarction (AMI). The main question it aims to answer is: Does an AI-based early warning system improve the assessment and prediction of adverse events across the full course of AMI care (from prevention to diagnosis, treatment, and rehabilitation)? Participants who are receiving routine medical care for AMI in tertiary hospitals will have their multimodal medical data (clinical records, diagnostic tests, imaging, treatment pathways) collected and analyzed. Data will be integrated using innovative cross-modal representation methods and predictive models. The study will follow patients during their hospital stay and subsequent clinical follow-up to evaluate the feasibility, accuracy, and clinical value of the AI-based early warning system.