Lung cancer is one of the deadliest malignant tumors globally, and innovative early diagnostic technologies are crucial for improving patient prognosis. This study innovatively integrates chemical sensors with generative artificial intelligence (AI) technologies to construct a research paradigm for the intelligent design of lung cancer-specific receptors and the optimization of sensor interfaces. In terms of technological innovation, on the one hand, an electrochemical sensing system based on nano-composite materials and an optical enhancement detection platform are built to achieve ultra-trace detection of lung cancer markers, aiming to break through the sensitivity bottleneck of traditional methods; On the other hand, multi-modal generative models are utilized to deeply mine multi-omics data, designing intelligent receptors with topological adaptability, significantly improving the accuracy and binding efficiency of biomolecule recognition. Clinical validation results show that this technology greatly enhances diagnostic efficacy in early lung cancer screening, and personalized treatment strategies based on AI effectively extend patient survival. In terms of technical translation and application, the developed portable detection devices and wearable monitoring technologies can reduce detection costs, providing a widely applicable screening solution for areas with limited medical resources. The study also reveals core challenges such as the explainability of generative AI and the environmental stability of sensors, proposing forward-looking directions such as quantum-biological interface integration and biomimetic adaptive sensing. This research establishes a new paradigm of "intelligent perception- dynamic optimization-precise intervention" for early lung cancer diagnosis, with significant clinical translational value.
扫码关注我们
求助内容:
应助结果提醒方式:
