Lateral Flow Assays for Rapid Disease Diagnosis: Importance, Challenges, and Strategies for Broader Adoption

Lateral flow assays are among the most important tools for rapid diagnosis and are playing an increasingly important role in healthcare systems, particularly in low-resource settings, decentralized diagnostic centers, and home-based applications. Owing to their simple operation, rapid turnaround time, relatively low cost, portability, minimal need for complex laboratory equipment, and visual interpretation of results, these assays have become suitable platforms for disease screening, preliminary diagnosis, and monitoring. A typical lateral flow assay consists of a sample pad, conjugate pad, nitrocellulose membrane, and absorbent pad. Through capillary-driven flow, these components enable specific biological reactions in different formats, including sandwich, competitive, and multiplex assays.

Despite these advantages, broader adoption of lateral flow assays still faces several challenges, including limited sensitivity, insufficient specificity, qualitative and sometimes subjective interpretation of results, limited quantitative capability, difficulties associated with complex biological samples, and the need for rigorous quality control and validation. In recent years, several strategies have been proposed to overcome these limitations, including optimization of reaction kinetics, improved membrane and pad design, use of advanced nanoparticles and labels, reduction of nonspecific binding, development of multiplex assays, and incorporation of reader devices and smartphone-based systems. In addition, connecting test results to digital platforms and health databases can expand the role of lateral flow assays from simple diagnostic tools to components of public health surveillance, telemedicine, and disease management infrastructures. Therefore, targeted development and optimization of this technology can support broader use of lateral flow assays as rapid, simple, portable, and reliable platforms for disease diagnosis.