Next-gen cancer therapy: The convergence of artificial intelligence, nanotechnology, and digital twin

Abstract

The combination of artificial intelligence [AI] and nanotechnology is revolutionizing cancer therapy by using precision medicine, enhancing early diagnosis, and optimizing drug delivery with a target. AI-driven nanocarriers are a next-generation platform for real-time biomarker identification, controlled drug release, and tailored treatment regimens that significantly augment the therapeutic effect and minimize systemic toxicity. Machine learning models aid rational nanomaterial design, predicting drug interactions, and formulating optimization for better bioavailability and tumor targeting. Quantum processing and AI-driven modeling are accelerating drug discovery, enhancing diagnostic accuracy, and automating clinical decisions. In addition, Digital Twin [DT] technology is turning out to be an oncology game-changer with virtual patient simulates that integrate genomic, clinical, and imaging data in order to forecast disease progression and tailor treatment. By bridging the gap between computer simulations and real-world clinical utilization, DTs allow for more effective treatment planning, dispense with trial-and-error approaches, and improve patient outcomes. However, major obstacles such as data harmonization, explainability of algorithms, regulation, and ethics remain challenges to large-scale uptake. Overcoming these constraints by interdisciplinary collaboration between researchers, clinicians, and regulatory bodies will be key to achieving the maximum potential of AI-based nanomedicine. This review explores the revolutionary impact of AI-driven nanocarriers and digital twin technology in cancer treatment, observing how they can transform cancer therapy through predictive analytics, intelligent drug delivery, and second-generation personalized therapy methods.

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