Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

IoT-enabled Smart Drug Delivery Systems for Personalized Medicine: Designing IoT-based drug delivery systems capable of tailoring medication administration schedules and dosages to individual patient needs, advancing the field of personalized medicine

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  • Dr. Carlos Sanchez
Dr. Carlos Sanchez
Associate Professor of Biomedical Engineering, Universidad de los Andes, Venezuela

Published 06-09-2024

Keywords

  • IoT,
  • healthcare
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Carlos Sanchez, “IoT-enabled Smart Drug Delivery Systems for Personalized Medicine: Designing IoT-based drug delivery systems capable of tailoring medication administration schedules and dosages to individual patient needs, advancing the field of personalized medicine”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 18–25, Sep. 2024, Accessed: Sep. 19, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/131

Abstract

The advent of IoT (Internet of Things) technology has revolutionized various industries, including healthcare. One of the most promising applications of IoT in healthcare is the development of smart drug delivery systems for personalized medicine. These systems leverage IoT devices to tailor medication administration schedules and dosages to individual patient needs, thereby enhancing treatment efficacy and patient outcomes. This paper presents a comprehensive overview of IoT-enabled smart drug delivery systems, focusing on their design, implementation, and impact on personalized medicine. We discuss the key components of these systems, including sensors, actuators, communication protocols, and data analytics algorithms. Furthermore, we highlight the benefits and challenges associated with the adoption of IoT in drug delivery, along with future research directions in this rapidly evolving field.

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References

  1. Saeed, A., Zahoor, A., Husnain, A., & Gondal, R. M. (2024). Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling. International Journal of Science and Research Archive, 12(2), 040-046.
  2. Shahane, Vishal. "A Comprehensive Decision Framework for Modern IT Infrastructure: Integrating Virtualization, Containerization, and Serverless Computing to Optimize Resource Utilization and Performance." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 53-75.
  3. Biswas, Anjanava, and Wrick Talukdar. "Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)." Journal of Science & Technology 4.6 (2023): 55-82.
  4. N. Pushadapu, “Machine Learning Models for Identifying Patterns in Radiology Imaging: AI-Driven Techniques and Real-World Applications”, Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 1, pp. 152–203, Apr. 2024
  5. Pelluru, Karthik. "Prospects and Challenges of Big Data Analytics in Medical Science." Journal of Innovative Technologies 3.1 (2020): 1-18.
  6. Chen, Jan-Jo, Ali Husnain, and Wei-Wei Cheng. "Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision." Proceedings of SAI Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
  7. Alomari, Ghaith, et al. “AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.” International Journal for Multidisciplinary Research, vol. 6, no. 3, May 2024, pp. 1–24.
  8. Choi, J. E., Qiao, Y., Kryczek, I., Yu, J., Gurkan, J., Bao, Y., ... & Chinnaiyan, A. M. (2024). PIKfyve, expressed by CD11c-positive cells, controls tumor immunity. Nature Communications, 15(1), 5487.
  9. Borker, P., Bao, Y., Qiao, Y., Chinnaiyan, A., Choi, J. E., Zhang, Y., ... & Zou, W. (2024). Targeting the lipid kinase PIKfyve upregulates surface expression of MHC class I to augment cancer immunotherapy. Cancer Research, 84(6_Supplement), 7479-7479.
  10. Gondal, Mahnoor Naseer, and Safee Ullah Chaudhary. "Navigating multi-scale cancer systems biology towards model-driven clinical oncology and its applications in personalized therapeutics." Frontiers in Oncology 11 (2021): 712505.
  11. Saeed, Ayesha, et al. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 1-9.
  12. Pelluru, Karthik. "Enhancing Cyber Security: Strategies, Challenges, and Future Directions." Journal of Engineering and Technology 1.2 (2019): 1-11.
  13. Mustyala, Anirudh, and Sumanth Tatineni. "Cost Optimization Strategies for Kubernetes Deployments in Cloud Environments." ESP Journal of Engineering and Technology Advancements 1.1 (2021): 34-46.

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