摘要Traditional imaging modalities often lack the molecular specificity and spatial resolution required for real-time tumor visualization,particularly in complex surgical settings.This narrative review examines the emerging class of human NAD(P)H:quinone oxidoreductase 1(hNQO1)-activatable fluorescent probes operating within the near-infrared-Ⅱ(NIR-Ⅱ,1,000-1,700 nm)and near-infrared-Ⅲ(NIR-Ⅲ,1,700-2,500 nm)windows,which offer transformative potential for tumor imaging and surgical guidance.hNQO1,overexpressed in various malig-nancies,including pancreatic,lung,and breast cancers,while minimally present in normal tissues,serves as an ideal biomarker for selective probe activation.However,its residual expression in certain normal tissues,such as the kidney and vascular endothelium,necessitates careful probe design to minimize off-target activation.This review systematically explores the design principles underlying hNQO1-activatable probes,emphasizing struc-tural features that enable enzyme-specific fluorescence activation with minimal background interference.Chemical synthesis strategies,including quinone propionate-based self-immolative linkers and advanced bio-conjugation techniques,are discussed alongside optimization approaches to enhance probe stability,sensitivity,and biocompatibility.The review highlights clinical applications in real-time tumor visualization,accurate margin delineation,early cancer detection,and therapeutic monitoring.Significantly,the integration of artificial intelligence(AI)and deep learning frameworks for automated image analysis,tumor segmentation,and radiomics-based feature extraction represents a critical advancement,transforming qualitative fluorescence signals into quantitative,clinically actionable data.Crucially,AI-derived quantitative biomarkers extracted from hNQO1-activated fluorescence,such as spatial heterogeneity indices,activation kinetics,and texture features,can be linked to clinical endpoints including treatment response prediction,recurrence risk stratification,and overall survival,providing a data-driven foundation for personalized oncology decision-making.Translational challenges including regulatory pathways,pharmacokinetics,safety considerations,model validation require-ments,and real-world clinical deployment frameworks are addressed,alongside future perspectives on ther-anostic applications and multimodal imaging integration.Together,hNQO1-activatable NIR-Ⅱ probes combined with intelligent computational systems represent a paradigm shift toward data-driven,high-precision cancer care.