Sepsis Diagnostics via Biosensors: Engineering Platforms, Artificial Intelligence Integration, and Clinical Translation

Sepsis remains one of the most diagnostically challenging syndromes due to its clinical heterogeneity, overlapping host-pathogen responses, and lack of a singular biomarker. Biosensors are redefining this landscape by moving beyond slow, culture-based methods toward real-time, multiplexed, and clinically validated diagnostic platforms. In this review, we synthesize advances across substrate engineering, nanomaterial-enabled amplification, and next-generation biorecognition elements, including aptamers, AMPs, PNAs, XNAs, and CRISPR systems, supported by real-world case studies that demonstrate clinical feasibility. Furthermore, the role of artificial intelligence in augmenting sepsis diagnostics is delineated by distinguishing models that enhance biosensor signal interpretation from broader EHR-driven prediction frameworks. A dedicated section consists of the translational bottlenecks: analytical-to-clinical performance gaps, regulatory barriers, diagnostic coding mismatches, and reimbursement challenges under DRG and NTAP pathways. Finally, we propose a forward-looking blueprint for accelerating biosensor deployment in sepsis care, emphasizing the need for harmonized clinical validation, design-for-deployment strategies, and collaborative innovation ecosystems. Unlike generic biosensor or AI reviews, this work advances explicitly against sepsis's diagnostic heterogeneity, offering a disease-centered translational roadmap.