Future Strategies for Medical Artificial Intelligence in Cardiometabolic Medicine: From Technical Constraints to a Socio-Technical Framework

The rapid evolution of medical artificial intelligence (AI) has demonstrated transformative potential in diagnosis and prognosis; however, its seamless integration into routine clinical practice remains a complex challenge.Given that cardiometabolic syndrome is characterized by the clustering of interrelated risk factors such as hypertension, diabetes mellitus, and dyslipidemia, the advanced multimodal integration of longitudinal time-series data and genomic profiles is essential to move from static risk scores toward dynamic, personalized prognostic modeling.This review analyzes the multi-dimensional barriers to AI adoption, including data heterogeneity, model opacity, and performance drift, and proposes an integrated strategy for sustainable deployment.From an ethical and legal standpoint, the lack of interpretability in AI systems undermines informed consent and complicates liability-issues that are particularly critical in cardiometabolic care where long-term treatment adherence is vital.To achieve successful implementation, comprehensive policy directions are required, including the enhancement of AI literacy, the development of publicly led data infrastructure, and the establishment of value-based reimbursement models that recognize the long-term preventive benefits of AI-driven interventions.Ultimately, the validity and effectiveness of medical AI in cardiometabolic syndrome depend on recognizing it as a socio-technical system that necessitates an integrated framework aligning technological standardization, dynamic regulation, and ethical governance with societal values.