Intelligent AI‑Driven Nanobots for Early Detection and Targeted Management of Esophageal Tumors: an Integrated Review of Nanotechnology, Artificial Intelligence, and Medical Nanorobotics
Keywords:
Esophageal Cancer, Intelligent Nanobots, Artificial Intelligence, Machine Learning, Nanotechnology, Early Tumor Detection, Targeted Drug Delivery, Medical Nanorobotics, Convolutional Neural Networks, Deep Reinforcement LearningAbstract
Esophageal cancer remains a major global health challenge due to late diagnosis, high mortality, and limitations of conventional detection methods. Current screening techniques, such as endoscopy, are often invasive and insufficient for early detection. Recent advances in nanotechnology, artificial intelligence (AI), and medical nanorobotics offer promising alternatives for improving diagnosis and treatment. Nanoparticle-based systems, including metal and polymeric nanoparticles, enable targeted drug delivery and enhanced cancer detection. Additionally, AI-driven approaches, such as convolutional neural networks (CNNs) and deep reinforcement learning (DRL), show potential in improving diagnostic accuracy and autonomous navigation within the esophagus. This review presents a novel hybrid framework integrating intelligent nanobots with AI techniques for precise tumor detection and targeted therapy. It also highlights global research trends and addresses key challenges through an AI-driven solution matrix, emphasizing the future potential of non-invasive, efficient, and patient-friendly approaches in esophageal cancer management.
DOI: https://doi.org/10.24321/3117.4787.202503
References
World Health Organization. (2024). Global Cancer Observatory: Esophageal Cancer Statistics. https:// www.who.int/cancer
Kong, X., Gao, P., Wang, J., Fang, Y., & Hwang, K. C. (2023). Advances of medical nanorobots for future cancer treatments. Journal of Hematology & Oncology,16(74), 1–45. https://doi.org/10.1186/s13045-023-01463-z
