The integration of robotics and artificial intelligence (AI) into nanomedicine represents a significant advancement in developing targeted therapeutic and diagnostic platforms. This field focuses on engineering micro- and nanoscale agents, such as magnetic nanoparticles (MNPs), microbots, and nanobots, for tasks like targeted therapies, sensing, and manipulation at diseased sites. MNPs are typically composed of iron oxides and serve as foundational components due to their biocompatibility, tunable surface chemistry, and responsiveness to external magnetic fields. They are used in targeted drug delivery, magnetic hyperthermia for tumor ablation, and as contrast agents in magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). Microbots and nanobots, which often incorporate MNPs for propulsion, can be actively guided using external magnetic fields to navigate complex biological environments, perform micromanipulation, and enable triggered drug release. The precise control of these magnetic agents relies on electromagnetic or permanent magnet-based guidance systems, which balance magnetic force strength, workspace volume, and clinical integration. Other classes like biohybrid microbots or DNA nanobots, utilize magnetic field independent mechanisms for molecular sensing and cargo delivery. AI and machine learning enhance these systems by optimizing material and bot design through in silico modeling, facilitating real-time navigation via medical imaging feedback, and enabling adaptive pathfinding. AI can also support swarm control and data analysis for diagnostic improvement. However, clinical translation faces challenges, including ensuring long-term biocompatibility and biodistribution, achieving scalable Good Manufacturing Practice (GMP) production, demonstrating therapeutic advantage in preclinical models, navigating evolving regulatory frameworks, and securing sufficient funding.
Luer et al. (Mon,) studied this question.