Akademik Veri Yönetim Sistemi
Electrochimica Acta, cilt.559, 2026 (SCI-Expanded, Scopus)
Cardiovascular diseases remain the leading cause of global morbidity and mortality, underscoring the urgent need guarantees for earlier detection, continuous monitoring, and personalized disease management. Recent advances in biosensor technologies have expanded the analytical landscape for cardiovascular diagnostics. However, many reported platforms remain confined to laboratory settings and lack clear pathways toward clinical translation. In this context, printed electrochemical biosensors have emerged as a particularly promising class of sensing systems, offering a unique combination of analytical performance, manufacturing scalability, cost efficiency, and compatibility with PoC systems. This review provides a critical and forward-looking analysis of artificial intelligence (AI)-enabled printed electrochemical biosensors for cardiovascular disease detection and monitoring. Rather than cataloging individual sensing strategies, the review examines how electrochemical transduction, printing technologies, and AI can be co-developed as integrated systems. Key sensing modalities, electrode architectures, surface functionalization strategies, and printed fabrication approaches are evaluated with respect to their translational relevance, including robustness, reproducibility, long-term stability, and clinical usability. AI is discussed not as a standalone diagnostic solution, but as an enabling analytical layer that enhances signal quality, personalization, multimodal data integration, and longitudinal interpretation. By synthesizing recent advances across electrochemistry, materials science, and data-driven analytics, this review highlights emerging design principles and unmet challenges that define the next generation of cardiovascular biosensing platforms. The analysis emphasizes a paradigm shift from episodic, single-analyte diagnostics toward continuous, system-level monitoring frameworks. Ultimately, this work positions printed electrochemical biosensors, augmented by responsible AI, as foundational technologies for scalable, interpretable, and clinically meaningful cardiovascular diagnostics.