Genetic Biosensors for High-Throughput Biomedical Applications: Challenges and Advances

Genetic biosensors represent a particularly promising class of bioengineered instruments designed to enable accurate, real-time monitoring of cellular and molecular processes, with especially strong relevance to high-throughput biomedical workflows. In this review, we provide a systematic, clearly structured overview of the field, covering the fundamental design principles that form the basis of today's platforms. We focus in particular on DNA-based, RNA-based, and CRISPR-based architectures while also examining engineered synthetic biology circuitry, and we place these foundational concepts in the context of more recent advances. These developments encompass improved sensitivity, increased scalability, and broader functionality for multiplexed detection. Beyond summarizing technological progress, we critically evaluate challenges that remain deeply embedded at both technical and biological levels, including limitations driven by signal-to-noise ratio constraints, practical difficulties in delivering probes into cells, and relatively restricted dynamic ranges. Taken together, these issues continue to hinder widespread, routine clinical adoption. Importantly, rather than presenting the topic only as a catalog of improvements, we argue for a unifying framework: the convergence of artificial intelligence and synthetic biology is actively reshaping biosensor engineering, moving it away from a largely empirical, trial-and-error approach toward a more reliable, data-driven discipline. To convert this conceptual shift into tangible outcomes, we also outline a practical implementation framework aimed at reducing the distance between research activities and real-world deployment. This framework emphasizes the need for scalable manufacturing processes, robust and reproducible clinical validation, and regulatory pathways capable of evolving as new evidence emerges. An integrated strategy of this kind is therefore essential for fully realizing the potential of real-time, multichannel biosensing platforms in both complex clinical diagnostics and point-of-care environments.