• Next-Generation Intelligent Medical Nanorobots and Nanogadgets with Artificial Intelligence for Targeted Cancer Therapy: Simulated Phagocytosis and Apoptosis Induction
  • Sahar Masoomi,1,*
    1. Researcher in Genetics, Pharmaceutical and Medical Biotechnology, Nanomedicine, and Medical Nanorobotics


  • Introduction: Intelligent medical nanorobots and nanogadgets represent a new generation of targeted cancer therapy tools that leverage nanotechnology and artificial intelligence to enable precise drug delivery to cancer cells. These technologies can overcome limitations of conventional treatments, such as drug side effects and insufficient access to target cells. Simulating cellular processes such as phagocytosis and apoptosis induction by nanorobots allows the evaluation of their functionality, safety, and efficacy prior to clinical testing. Nanorobots, using self-propelled or guided mechanisms and biomarker sensors, can identify and transport drugs to target cells. These nanorobots and nanogadgets are designed and implemented to operate at nano- to microscale dimensions, approximately matching the size of cancer cells, enabling simultaneous drug delivery, cancer cell recognition, and induction of apoptosis and phagocytosis. The integration of artificial intelligence with these nanorobots facilitates real-time biological data analysis, prediction of movement paths, and optimization of drug delivery, which significantly enhances the potential for personalized and efficient cancer therapy.
  • Methods: This study is a systematic and analytical review of articles published between 2018 and 2025, examining the application of intelligent medical nanorobots and nanogadgets in targeted cancer therapy. Selected articles focus on the design and simulation of smart nanorobots and nanogadgets, AI-based predictive models for optimizing drug delivery, and mechanisms for targeting cancer cells. Database searches were conducted in PubMed, Scopus, Web of Science, and SpringerLink using keywords such as “nanorobots,” “nanogadgets,” “artificial intelligence,” “targeted drug delivery,” and “cancer therapy.” Extracted data included design features of nanorobots and nanogadgets, movement and targeting mechanisms, AI-based simulation and modeling methods, and therapeutic performance outcomes in laboratory and preclinical models. In addition to the literature review, this study emphasizes an innovative idea from the author: simulating phagocytosis and apoptosis induction by intelligent nanorobots to optimize drug delivery to cancer cells, derived from analysis and synthesis of review articles and prior research experience. This approach allows assessment of nanorobot and nanogadget efficacy and safety in complex cellular environments before clinical trials and facilitates the development of AI-based personalized therapies.
  • Results: Reviewed studies and simulations demonstrate that intelligent nanorobots can deliver drugs to cancer cells with high precision and induce apoptosis. Preclinical results indicate reduced side effects and increased therapeutic efficiency compared to conventional methods. Moreover, AI-based simulation models can predict optimal drug delivery strategies and nanorobot movement paths, reducing errors and improving treatment success.
  • Conclusion: Intelligent nanorobots and nanogadgets, developed through the convergence of nanotechnology and artificial intelligence, represent a transformative strategy in the field of targeted cancer therapy. By leveraging nanoscale precision and adaptive algorithms, these systems can be engineered to navigate the tumor microenvironment, recognize malignant cells based on molecular signatures, and selectively deliver therapeutic agents while minimizing off-target effects. Their design incorporates the ability not only to transport drugs but also to induce apoptosis, regulate tumor-related signaling pathways, and monitor treatment efficacy in real time. Such multifunctional capabilities provide a promising preclinical framework for assessing both therapeutic performance and biosafety. Furthermore, these nanosystems have the potential to address long-standing challenges in oncology, including overcoming drug resistance, enhancing the bioavailability of therapeutic compounds, and achieving controlled release at the cellular and subcellular levels. The integration of AI-driven simulations with empirical data from in vitro and in vivo models will be essential for optimizing parameters such as targeting accuracy, dosage control, and treatment personalization. Future research should also emphasize large-scale biocompatibility studies, regulatory standardization, and the development of clinical trial protocols to bridge the gap between laboratory innovation and real-world application. Ultimately, the intelligent design of nanorobots and nanogadgets offers a pathway toward more precise, less invasive, and highly individualized cancer treatments, opening new horizons for next-generation oncology.
  • Keywords: Nanorobots; Nanogadgets; Artificial Intelligence; Targeted Cancer Therapy; Apoptosis