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Cancer is a life-threatening disease, resulting in nearly 10 million deaths worldwide. There are various causes of cancer, and the prognostic information varies in each patient because of unique molecular signatures in the human body. However, genetic heterogeneity occurs due to different cancer types and changes in the neoplasms, which complicates the diagnosis and treatment. Targeted drug delivery is considered a pivotal contributor to precision medicine for cancer treatments as this method helps deliver medication to patients by systematically increasing the drug concentration on the targeted body parts. In such cases, nanoparticle-mediated drug delivery and the integration of artificial intelligence (AI) can help bridge the gap and enhance localized drug delivery systems capable of biomarker sensing. Diagnostic assays using nanoparticles (NPs) enable biomarker identification by accumulating in the specific cancer sites and ensuring accurate drug delivery planning. Integrating NPs for cancer targeting and AI can help devise sophisticated systems that further classify cancer types and understand complex disease patterns. Advanced AI algorithms can also help in biomarker detection, predicting different NP interactions of the targeted drug, and evaluating drug efficacy. Considering the advantages of the convergence of NPs and AI for targeted drug delivery, there has been significantly limited research focusing on the specific research theme, with most of the research being proposed on AI and drug discovery. Thus, the study's primary objective is to highlight the recent advances in drug delivery using NPs, and their impact on personalized treatment plans for cancer patients. In addition, a focal point of the study is also to highlight how integrating AI, and NPs can help address some of the existing challenges in drug delivery by conducting a collective survey.

We present a new 3D lens rendering technique and a new spatiotemporal lens. Interactive 3D lenses, often called volumetric lenses, provide users with alternative views of data sets within 3D lens boundaries while maintaining the surrounding overview (context). In contrast to previous multipass rendering work, we discuss the strengths, limitations, and performance costs of a single-pass technique especially suited to fragment-level lens effects, such as color mapping, lighting, and clipping. Some object-level effects, such as a data set selection lens, are also incorporated, with each object's geometry being processed once by the graphics pipeline. For a substantial range of effects, our approach supports several composable lenses at interactive frame rates without performance loss during increasing lens intersections or manipulation by a user. Other cases, for which this performance cannot be achieved, are also discussed. We illustrate possible applications of our lens system, including Time Warp lenses for exploring time-varying data sets.

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