AI-driven nanomedicine for cancer theranostics

Abstract

Nanomedicine's merging with artificial intelligence (AI) is fundamentally changing cancer theranostics through precise creation of multifunctional nanoparticles which can simultaneously diagnose and treat diseases. Traditional cancer treatments today face issues with imprecise delivery and general body toxicity as well as late-stage disease recognition which theranostic nanoplatforms address through precision drug transport and live imaging functions. In this analysis, we examine the existing state and forthcoming developments of AI applications in cancer theranostics through nanoparticles. Our study first examines the three primary classes of theranostic nanomaterials that show clinical significance: liposomes, gold nanoparticles, iron oxide nanoparticles, and quantum dots. AI is being applied to nanoparticle development through machine learning, deep learning, reinforcement learning, and generative models that support physicochemical predictions, synthesis optimization, biodistribution modeling, and inverse design. We analyze clinical applications of AI solutions which support patient identification, response predictions, and implementation of virtual patient models for individualized cancer treatment. The paper evaluates major difficulties which are nanotoxicity, AI explainability, data limitations, and regulatory concerns while addressing ethical dilemmas. Rather than a broad overview of nanomedicine, we center on AI methods that directly improve theranostic decisions and support this with worked exemplars reporting datasets, baselines, metrics, and clinical tie-ins. This Task–Data–Method–Metric lens replaces generic background and grounds claims in reproducible evidence.

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