Beyond Traditional Screening: The Future of Heart Failure Detection With Biomarkers and AI

Heart failure (HF) continues to be a leading cause of death and disability globally, with an estimated annual prevalence of over 56 million patients and a projected significant increase in occurrence. Although drug treatments based on existing guidelines can enhance the clinical prognosis for patients with HF, the five‐year survival rate remains below 50%. This underscores the critical need for early detection and effective strategies to prevent and manage the progression of HF. Present screening approaches, encompassing individual biomarkers and imaging techniques, exhibit shortcomings in identifying subclinical or incipient‐stage diseases. This review amalgamates the most recent advancements in HF screening, specifically highlighting novel biomarkers that correspond to various pathways involved in cardiac remodeling, inflammation, neurohormones, and emerging pathophysiological pathways. Several emerging biomarkers, notably sST2 and GDF‐15, demonstrate strong potential for inclusion in future clinical guidelines, enhancing early risk stratification and personalized screening. Artificial intelligence‐driven approaches, integrating electrocardiograms, wearable devices, and analysis of medical images using machine learning and multimodal data, represent a promising strategy for improving early risk identification. Nevertheless, it is imperative for researchers to systematically address enduring challenges. These challenges encompass analytical variability in machine learning data, the absence of multicenter clinical trial data, constraints in hardware deployment, and biases prevalent in resource‐limited settings. Future research endeavors should prioritize the integration of multi‐omics technologies, thereby enhancing the precision of biomarkers. This would enhance biomarker accuracy, facilitate dynamic risk stratification for HF, and guarantee equitable global implementation. Combining evidence‐based methodologies with technological innovation will be fundamental to developing scalable screening tools. Implementing such a stepwise integrated screening framework will allow clinicians to optimize therapeutic timing and improve survival outcomes in HF management.