Artificial intelligence for CRISPR-based biosensing: From molecular design to assay intelligence

CRISPR-based biosensors are emerging as powerful tools in precision medicine, enabling rapid and highly specific molecular detection for personalized interventions. Their performance depends on interconnected components. Guide RNA (gRNA) design and optimization improve on-target activity and reduce off-target effects, while Cas protein engineering and structure prediction reveal functional determinants for programmable modification. Meanwhile, biosensor design and signal readout strategies enable portable, quantitative, and multiplexed detection. Recent advances in artificial intelligence (AI) have created new opportunities to optimize these components. AI bridges molecular design and analytical performance, enhancing sensitivity, specificity, quantitative readout, and automation in CRISPR-based biosensing. This paper provides a systematic review and comparative analysis of recent progress in AI-enabled gRNA design and optimization, Cas protein engineering and structure prediction, and biosensor readout and automation for CRISPR-based biosensing. It further discusses the opportunities and challenges associated with integrating these advances into multifunctional, standardized platforms for point-of-care testing and clinical translation.