Over the last few years, minimally invasive operations have improved drastically due to medical robotics, which has transformed how complex operations are conducted. The InTarget project, which wants to take medical robotics to the next level, seeks EPSRC financing to build a robotic-based system capable of intelligent magnetic field control for targeted drug delivery (Al-Mamun et al., 2018; Sadeghi et al., 2020). But this proposal seeks to construct a robotic system that addresses current drug delivery methods' drawbacks, increasing patient safety and treatment efficacy.

Current invasive therapies can cause difficulties despite minimally invasive ones. Robotic-based systems for drug delivery can enable precise, non-invasive delivery of medication to precise physical targets. This mitigates complications from off-target impacts and open surgery. Magnetic fields can control drug delivery systems, thanks to technological advances in robotics. Liu et al. Welcome this technology so clinicians may give medication more accurately, improving patient safety and decreasing adverse effects. Machine learning can improve robotic drug delivery devices by facilitating real-time monitoring. The Artificial Intelligence-powered technology can adapt to patient-specific conditions and variables during the operation to improve its accuracy, efficacy, and safety. These systems may change magnetic force and other production factors to ensure constant synchronization (Wang et al., 2021). Thus, minimally invasive operations have become standard and offer several advantages, including fewer complications compared to traditional invasive methods. Robotics and machine learning give new advantages and opportunities to advanced medical treatments should developments in medicine continue. It is an effort greatly required by our world today.

References

Al-Mamun, A., Khanam, S., Rahman, M. F., Ullah, M. S., Nazuous, A. H. I. E., Khan, B. S., & Istiaq, A. (2018). Minimally invasive surgery: A review. Journal of Surgical Research, 229, 90–100. https://doi.org/10.1016/j.jss.2018.03.015

Liu, K. J., Zheng, P., Ma, L., Qiu, F., Xu, Y., Ding, L., Wu, W., & Jiang, W. (2021). Application of magnetic targeting therapy with multifunctional magnetic nanoparticles in periarticular osteolysis in a rabbit model. International Journal of Nanomedicine, 16, 5751–5769. https://doi.org/10.2147/ijn.s314048

Sadeghi, K. T., Gharahdaghi, T., & Karamalkov, A. (2020). Magnetic artificial periodic bionic system for smart drug delivery. Journal of Biomedical Materials Research Part A, 108(11), 2335–2341. https://doi.org/10.1002/jbm.a.36889

Wang, Y., Wei, Z., Wu, B., Zhang, L., Yang, J., Zhang, X., Xu, R., Chen, S., & Nebe, J. B. (2021). Machine learning prediction of titanium surface roughness on the osseointegration of endosseous implants in preclinical rabbits. Frontiers in Bioengineering and Biotechnology, 8, 694871. https://doi.org/10.3389/fbioe.2020.694871

Machine Learning and Robotics for Advanced Medical Applications: Developing Robotic-Based Systems for Targeted Drug Delivery