Synthetic biology is transforming biosensors from passive molecular detectors into programable systems capable of integrating biological sensing with therapeutic decision-making. Conventional biosensors respond to a single molecular input and are inherently limited in interpreting complex biological environments that characterize human disease. Synthetic biology logic gates address this limitation by implementing Boolean computation through modular transcriptional regulators, RNA switches, CRISPR-based regulators, and recombinase systems, enabling autonomous biological decision-making within living cells. These genetic circuits enable biosensors to evaluate multiple disease-relevant inputs simultaneously and generate context-specific diagnostic or therapeutic outputs. This review critically analyzes how logic-gated biosensing systems integrate biological sensing, genetic computation, and therapeutic actuation, while identifying the engineering and translational barriers that currently limit their clinical implementation. Comparison of current platforms shows that increasing circuit complexity generally improves disease specificity and therapeutic precision but often reduces robustness, scalability, and translational feasibility because of circuit leakage, metabolic burden, crosstalk, and biological variability. While AND and SynNotch circuits provide superior target discrimination for cell therapies, CRISPR-based and memory-enabled systems expand programable control but introduce additional engineering challenges. Current clinical translation remains constrained by circuit stability, manufacturing complexity, biosafety, and the lack of standardized regulatory frameworks. Emerging modular circuit design, cell-free biosensing, and artificial intelligence-assisted optimization are beginning to address these limitations and provide more scalable strategies for precision therapeutics. Collectively, this review identifies the engineering principles and design trade-offs that will govern the development of next-generation logic-enabled biosensors from proof-of-concept platforms to clinically deployable therapeutic systems.