Sharma Prashant Kishor, Chen Chia-Yuan
Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Biosensors (Basel). 2025 Dec 2;15(12):793. doi: 10.3390/bios15120793.
The integration of artificial intelligence (AI) and micro/nanorobotics is fundamentally reshaping biosensing by enabling autonomous, adaptive, and high-resolution biological analysis. These miniaturized robotic systems fabricated using advanced techniques such as photolithography, soft lithography, nanoimprinting, 3D printing, and self-assembly can navigate complex biological environments to perform targeted sensing, diagnostics, and therapeutic delivery. AI-driven algorithms, mainly those in machine learning (ML) and deep learning (DL), act as the brains of the operation, allowing for sophisticated modeling, genuine real-time control, and complex signal interpretation. This review focuses recent advances in the design, fabrication, and functional integration of AI-enabled micro/nanorobots for biomedical sensing. Applications that demonstrate their potential range from quick point-of-care diagnostics and in vivo biosensing to next-generation organ-on-chip systems and truly personalized medicine. We also discuss key challenges in scalability, energy autonomy, data standardization, and closed-loop control. Collectively, these advancements are paving the way for intelligent, responsive, and clinically transformative biosensing systems.
人工智能（AI）与微纳机器人技术的融合正在从根本上重塑生物传感，实现自主、自适应和高分辨率的生物分析。这些使用光刻、软光刻、纳米压印、3D打印和自组装等先进技术制造的小型化机器人系统，可以在复杂的生物环境中导航，以执行靶向传感、诊断和治疗递送。人工智能驱动的算法，主要是机器学习（ML）和深度学习（DL）中的算法，充当操作的大脑，实现复杂建模、真正的实时控制和复杂信号解释。本综述重点介绍了用于生物医学传感的人工智能微纳机器人在设计、制造和功能集成方面的最新进展。展示其潜力的应用范围从快速即时诊断和体内生物传到下一代芯片器官系统以及真正的个性化医疗。我们还讨论了在可扩展性、能源自主性、数据标准化和闭环控制方面的关键挑战。总的来说，这些进展正在为智能、响应性和临床变革性的生物传感系统铺平道路。
