Advancing point-of-care diagnostics: Engineering and enhancing sensitivity and specificity in nanoparticle-based lateral flow assays for rapid disease detection.

Lateral flow assay (LFA) is the most widely used point-of-care (PoC) diagnostic tools due to their simplicity, rapid turnaround, portability, and low cost. Despite their extensive adoption in clinical diagnostics, food safety, and environmental monitoring, conventional LFA remain limited by inadequate sensitivity, poor quantification, and restricted multiplexing capability particularly for early-stage disease detection. This review systematically examines recent engineering strategies developed to overcome these limitations and advance LFA toward next-generation diagnostic platforms. We highlight progress in biorecognition element engineering, including antibodies, nanobodies, and aptamers, alongside innovations in nanomaterial-enabled engineering through advanced nanoparticle labels, signal reporters, such as nanozymes, fluorescent probes to improve signal amplification systems. Emerging approaches for sample preconcentration, nucleic acid amplification, and multiplex assay architectures are critically discussed in the context of analytical sensitivity enhancement and improving specificity. Importantly, recent integration of artificial intelligence (AI) and smartphone-based readout systems has transformed LFA into digitally enabled diagnostics, allowing objective interpretation, wireless data transmission and semi-quantitative analysis while preserving portability and low manufacturing cost. Collectively, these advances position modern LFA as adaptable, cost-effective, and scalable diagnostic tools capable of bridging the gap between rapid field tests and centralized laboratory assays, with significant implications for decentralized healthcare, large-scale screening, and global disease surveillance.