Advancements in nanophotonics and smart nanomaterials integrated with artificial intelligence-driven gene editing: A paradigm shift in cancer diagnosis and therapeutic

Abstract

Traditional cancer therapies are limited by side effects and damage to healthy tissues, while modern targeted treatments face challenges such as drug resistance and restricted applicability across cancer types. Early diagnosis also remains difficult, as many methods lack the sensitivity and specificity needed to reliably detect small, early-stage tumors. This review explores hybrid nanomaterial-based delivery systems, such as lipid–gold nanoparticle composites combined with polymeric nanocarriers, to improve the precision and efficacy of gene therapy. Advances in nanotechnology are highlighted for their ability to augment gene-editing tools including RNA interference and clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9), supported by techniques like optical tweezers, plasmonics, fluorescence imaging, and metamaterials. Nanophotonics in particular offers ultra-sensitive molecular imaging and real-time biomarker detection, underscoring its value for early cancer diagnosis. Artificial intelligence further strengthens these approaches by optimizing nanocarrier design, predicting therapeutic outcomes, and guiding personalized treatment strategies. Machine learning and deep learning platforms enable efficient analysis of complex genomic and clinical datasets, improving predictive accuracy and therapeutic customization. The review also outlines molecular mechanisms of gene therapy, from editing to expression, and addresses barriers to clinical translation, such as data integration, model validation, and regulatory considerations. Combining nanotechnology, artificial intelligence (AI), and gene-editing advances holds promise for more effective, targeted, and minimally invasive cancer treatments. These integrated strategies support earlier detection, enhance therapeutic precision, and provide a framework for translating experimental breakthroughs into clinical applications that better align with the goals of personalized medicine.