NCT07367399

Acute Myocardial Infarction Clinical Intelligent Decision Support System

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 15,000 (estimated)
• Sponsor: Beijing Anzhen Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

Acute Myocardial Infarction (AMI) remains the leading cause of cardiovascular mortality globally. In China, while the incidence of AMI is escalating at an annual rate of 5.2%, significant clinical challenges persist: diagnostic delays in primary care facilities exceed 40%, and the "Door-to-Balloon" (D2B) compliance rate in tertiary hospitals stagnates at a mere 65%. These figures underscore systemic deficiencies, including inefficient emergency response, regional resource disparities, and fragmented longitudinal care. Although Large Language Models (LLMs) provide a transformative technical foundation for AMI management, their clinical translation is hindered by critical bottlenecks, such as non-standardized data interfaces, limited model interpretability, inadequate hardware infrastructure at the grassroots level, and the inherent tension between data privacy and training requirements. This research proposes a comprehensive implementation strategy for an AI-driven intelligent decision-making system for AMI. On a theoretical level, the study establishes a tripartite framework of "Technological Adaptation, Scenario Implementation, and Safeguard Mechanisms." By introducing a data governance scheme based on federated learning and multimodal fusion, and constructing a "Technical-Clinical-Economic" multidimensional evaluation model, this work bridges the theoretical divide between advanced technology and clinical practice. On a practical level, the study develops adaptive gateways and lightweight models to facilitate pervasive deployment in resource-constrained settings, optimizes the full-cycle clinical workflow to improve patient outcomes, and provides a scalable, replicable pathway for implementation. Focusing on four core challenges-technological compatibility, clinical workflow integration, the balance between privacy and performance, and the establishment of scientific evaluation systems-this research aims to surmount existing translation barriers. It seeks to enhance the quality and efficiency of AMI care while providing a seminal reference for the clinical transformation of AI in other medical specialties.

Conditions

• Acute Myocardial Infarction
• Large Language Models
• Clinical Decision Support System

Timeline

• Start date: 2018-01-01
• Primary completion: 2028-12-31
• Completion: 2028-12-31
• First posted: 2026-01-26
• Last updated: 2026-01-26

Locations

1 site across 1 country: China