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  • Combined application of nanotechnology and artificial intelligence in cancer radiology: A systematic review

  • Hasan Ahmadzadeh Rishehri,1,*
    1. Graduated from the Faculty of Veterinary Medicine, Shiraz University, Shiraz, Iran


  • Introduction: Early and accurate cancer diagnosis has always been one of the fundamental challenges of modern medicine. In recent years, two leading technological fields, nanotechnology and artificial intelligence, have separately created revolutions in cancer radiology, but what has received less attention is the enormous potential of combining these two fields. Nanotechnology, by providing nanoparticle-based contrast agents, nanosensors and targeted nanocarriers, enables molecular imaging with unprecedented sensitivity and specificity, while artificial intelligence, especially deep learning, has the power to extract complex patterns and quantify large amounts of image data. The aim of this systematic review is to identify and synthesize the existing evidence on combined strategies of nanotechnology and artificial intelligence in cancer radiology and to evaluate the role of this integrated approach in improving diagnostic accuracy, reducing radiation dose and personalizing oncology imaging.
  • Methods: This systematic review was conducted according to the Cochrane Institute guidelines and the PRISMA guidelines. A comprehensive and systematic search was conducted in internationally recognized databases including PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar search engine for articles published between January 2020 and April 2026. The search strategy was designed using a combination of key keywords including "nanotechnology", "artificial intelligence", "machine learning", "deep learning", "radiology", "cancer imaging", "nanoparticle contrast agents", and "molecular imaging". The inclusion criteria included original articles with experimental, preclinical (in vivo/in vitro) designs, and phase I and II clinical trials that simultaneously used a nanotechnology (to enhance imaging) and an artificial intelligence algorithm (to analyze or optimize images). Reviews, case reports, and articles that used only one of the two technologies were excluded from the analysis. Finally, out of the initial 528 articles, after removing duplicates and screening the title and abstract, 31 articles were identified as eligible for the final analysis.
  • Results: The findings of this review showed that combined nanotechnology and AI approaches in cancer radiology can be classified into three main strategic categories. First, the use of AI algorithms to design and optimize imaging nanoparticles; In these studies, machine learning models, by analyzing large libraries of physicochemical properties of nanoparticles (size, shape, surface coverage, and surface charge), have been able to predict which nanocomposite will provide the highest image contrast and the lowest cytotoxicity in animal models of breast and lung tumors, reducing the time to develop the optimal nanoprobe from months to days. A second key strategy is to use artificial intelligence to interpret and analyze radiological images obtained from the injection of tumor-specific nanoparticles; in this context, studies have shown that magnetic resonance images (MRI) obtained after the injection of iron oxide nanoparticles coated with anti-EGFR antibodies, when processed by a deep convolutional neural network, are able to distinguish malignant from benign lesions with an accuracy that is significantly higher than the traditional interpretation of these images by an experienced radiologist or conventional quantitative analysis. The third and most advanced level of integration is the simultaneous use of injectable nanosensors (which produce an image signal dependent on the tumor environment, such as acidic pH or the presence of a specific enzyme) and recurrent neural networks that can decode these complex and dynamic signals over time and draw a 3D map of nanoparticle penetration into the tumor mass and micrometastases that are not visible with conventional methods.
  • Conclusion: This systematic review demonstrates that the integration of nanotechnology and artificial intelligence in cancer radiology is an emerging and extremely promising field that offers three main advantages over either technology alone; increased diagnostic sensitivity and specificity through intelligent molecular targeting, reduced radiation dose and volume of contrast agent required by the patient, and improved
  • Keywords: Nanotechnology, Artificial Intelligence, Cancer Radiology, Molecular Imaging, Targeted Nanoparticles

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