心不全におけるデジタルツインと人工知能：前提と将来性

Heart failure care has advanced, yet outcomes remain inconsistent and clinical deterioration is still recognized too late. Traditional Artificial Intelligence (AI) has evolved from rule-based, predictive, to generative and generally operates at the population level. Thus, it cannot keep pace with the rapidly shifting physiologic states that define heart failure. Digital twin technology offers a decisive shift: a continuously calibrated, mechanistically grounded computational replica of an individual patient that integrates multimodal physiologic, imaging, clinical, molecular, and behavioral data. Anchored in physics-informed models and paired with advanced AI layers, the digital twin functions as a real-time simulator rather than a static predictor. It enables four transformative applications: early physiologic instability forecasting, virtual comparator control arms for pragmatic trials, mechanism-anchored phenotyping, and system-level resource optimization. If built with rigor, equity, and transparent validation, digital twins can transition heart failure care from reactive management to anticipatory, individualized, and mechanistically informed decision-making, bringing a long-sought precision to this complex syndrome. Yet, this staged translation will require responsible, evidence-based implementation and precise recognition of limitations.