Convergence of multiplexed immunosensors, nanotechnology, and AI for early pancreatic cancer diagnosis.

Pancreatic cancer (PC) remains one of the deadliest malignancies, largely because of its asymptomatic onset, rapid progression, and absence of sensitive, specific tools for early detection at the population level. Conventional biomarkers such as carbohydrate antigen 19-9 (CA19-9) and standard imaging modalities offer limited performance in identifying pre-malignant or early-stage disease, underscoring the urgent need for next-generation diagnostic strategies. This narrative review examines the emerging convergence of multiplexed immunosensors, nanotechnology-enabled signal amplification, and artificial intelligence (AI)-driven data integration as a transformative framework for early PC diagnosis. We first summarize the evolving biomarker landscape, including circulating proteins, autoantibodies, exosomes, circulating tumor DNA, noncoding RNAs, and tumor-educated platelets, with an emphasis on multianalyte panels that capture tumor heterogeneity and tumor-host interactions. We then discuss the principles and design of multiplexed immunosensors across electrochemical, optical, and microfluidic formats and describe how nanomaterials such as nanoparticles, nanowires, quantum dots, and two-dimensional materials enhance analytical performance through increased surface area, improved biorecognition density, and engineered transduction mechanisms. Building on this foundation, we highlight integrated nanoimmunosensor platforms that combine microfluidics, lab-on-a-chip architectures and point-of-care configurations to enable low-volume, rapid, and potentially noninvasive testing from blood, saliva, or other biofluids. Finally, we explore how machine learning and deep learning models can fuse high-dimensional biosensor outputs with clinical, radiological, and genomic data to achieve robust feature selection, risk stratification, and individualized probability estimates for early PC.