AI and Machine Learning in Healthcare Robotics: Exploring AI-driven algorithms and their role in addressing healthcare challenges, including task optimization and patient care

Authors

  • Prof. Lucas Ramirez Professor of Machine Learning Applications, National University of Sciences and Technology, Pakistan

Keywords:

Swarm Intelligence, Robotics, Task Allocation, Exploration, Algorithms, Collective Behavior, Social Insects, Efficiency, Dynamic Environments, Robustness

Abstract

Swarm intelligence (SI) has emerged as a promising paradigm for solving complex problems inspired by the collective behavior of social insects. In the field of robotics, SI algorithms have been applied to various tasks, including task allocation and exploration. This paper provides a comprehensive analysis of swarm intelligence in robotics, focusing on the principles of SI, types of algorithms, and their applications. We discuss how SI algorithms can be used to enhance the capabilities of robotic systems, improve efficiency, and achieve robustness in dynamic environments. Through a review of recent research and case studies, we highlight the benefits and challenges of implementing SI in robotics and explore future directions for research in this exciting field.

References

Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.

Buddha, Govind Prasad, and Rahul Pulimamidi. "The Future Of Healthcare: Artificial Intelligence's Role In Smart Hospitals And Wearable Health Devices." Tuijin Jishu/Journal of Propulsion Technology 44.5 (2023): 2498-2504.

Bayraktar, Necmi. "Non-invasive alternative for phosphodiesterase inhibitor-refractory erectile dysfunction: Real-life experience with low-intensity extracorporeal shockwave therapy." Medicine 102.45 (2023): e35939.

Bayraktar, Necmi, and Fazil Tuncay Aki. "Laparoscopy-assisted peritoneal dialysis catheter placement using a modified minimally invasive approach: A retrospective observational study." Medicine 102.43 (2023): e35814.

Bayraktar, Necmi. "Prevalence of Family Refusal and Associated Factors in Declared Brain Death: A Six-Year Retrospective Study in Northern Cyprus." Transplantation Proceedings. Vol. 55. No. 7. Elsevier, 2023.

Kolay, Srikanta, Kumar Sankar Ray, and Abhoy Chand Mondal. "K+ means: An enhancement over k-means clustering algorithm." arXiv preprint arXiv:1706.02949 (2017).

Ray, Kumar S., and Srikanta Kolay. "Application of Approximate Equality for Reduction of Feature Vector Dimension." Journal of Pattern Recognition Research 1 (2016): 26-40.

Varela, Damián Tuset. "Artificial Intelligence on the Global Stage: Transforming Diplomacy and International Relations." Advances in Deep Learning Techniques 4.1 (2024): 53-57.

Varela, Damián Tuset. "AI Arms Races: Implications for Global Stability." Journal of Computational Intelligence and Robotics 1.2 (2021): 1-5.

Varela, Damián Tuset. "Artificial Intelligence in Humanitarian Aid and Development: A New Paradigm for International Cooperation." Journal of Artificial Intelligence Research 1.2 (2021): 1-4.

Varela, Damián Tuset. "Navigating Cyber Diplomacy in the Governance of Emerging AI Technologies: Lessons from Transatlantic Cooperation." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2.1 (2024): 110-124.

Varela, Damián Tuset. "Diplomacy in the Age of AI: Challenges and Opportunities." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2.1 (2024): 98-109.

Varela, Damián Tuset. "El Derecho en el siglo XXI: de lineal a circular." Diario La Ley 10409 (2023): 3.

Dey, Sudipto, et al. "Methods and systems for selecting a machine learning algorithm." U.S. Patent Application No. 18/514,181.

Dey, Sudipto, and Pulla Reddy P. Yeduru. "Methods and systems for predicting prescription directions using machine learning algorithm." U.S. Patent Application No. 18/242,098.

Dey, Sudipto, et al. "Methods and systems for automatic prescription processing using machine learning algorithm." U.S. Patent No. 11,848,086. 19 Dec. 2023.

Dey, Sudipto, and Pulla Reddy P. Yeduru. "Methods and systems for predicting prescription directions using machine learning algorithm." U.S. Patent No. 11,783,186. 10 Oct. 2023.

Dey, Sudipto, et al. "Microservice architecture with automated non-intrusive event tracing." U.S. Patent Application No. 17/499,966.

Dey, Sudipto, and Pulla Reddy P. Yeduru. "Methods and systems for predicting prescription directions using machine learning algorithm." U.S. Patent No. 11,468,320. 11 Oct. 2022.

Dey, Sudipto, and Pulla Reddy P. Yeduru. "Methods and systems for predicting prescription directions using machine learning algorithm." U.S. Patent No. 11,468,320. 11 Oct. 2022.

Veronin, Michael A., et al. "Opioids and frequency counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database: A quantitative view of the epidemic." Drug, Healthcare and Patient Safety (2019): 65-70.

Dixit, Rohit R., Robert P. Schumaker, and Michael A. Veronin. "A Decision Tree Analysis of Opioid and Prescription Drug Interactions Leading to Death Using the FAERS Database." IIMA/ICITED Joint Conference 2018. INTERNATIONAL INFORMATION MANAGEMENT ASSOCIATION, 2018.

Veronin, Michael A., Robert P. Schumaker, and Rohit Dixit. "The irony of MedWatch and the FAERS database: an assessment of data input errors and potential consequences." Journal of Pharmacy Technology 36.4 (2020): 164-167.

Schumaker, Robert P., et al. "Calculating a Severity Score of an Adverse Drug Event Using Machine Learning on the FAERS Database." IIMA/ICITED UWS Joint Conference. INTERNATIONAL INFORMATION MANAGEMENT ASSOCIATION, 2017.

Veronin, Michael A., et al. "A systematic approach to'cleaning'of drug name records data in the FAERS database: a case report." International Journal of Big Data Management 1.2 (2020): 105-118.

Dixit, Rohit R. "Predicting Fetal Health using Cardiotocograms: A Machine Learning Approach." Journal of Advanced Analytics in Healthcare Management 6.1 (2022): 43-57.

Schumaker, Robert P., Michael A. Veronin, and Rohit R. Dixit. "Determining Mortality Likelihood of Opioid Drug Combinations using Decision Tree Analysis." (2022).

Dixit, Rohit R. "Risk Assessment for Hospital Readmissions: Insights from Machine Learning Algorithms." Sage Science Review of Applied Machine Learning 4.2 (2021): 1-15.

Veronin, Michael A., et al. "Irony of the FAERS Database: An Analysis of Data Input Errors and Potential Consequences." IIMA/ICITED Joint Conference 2018. INTERNATIONAL INFORMATION MANAGEMENT ASSOCIATION, 2018.

Schumaker, Robert P., et al. "A data driven approach to profile potential SARS-CoV-2 drug interactions using TylerADE." Journal of International Technology and Information Management 30.3 (2021): 108-142.

Dossa, Kossivi Fabrice, et al. "Economic analysis of sesame (Sesamum indicum L.) production in Northern Benin." Frontiers in Sustainable Food Systems 6 (2023): 1015122.

Dossa, Kossivi Fabrice, and Yann Emmanuel Miassi. "Exploring the nexus of climate variability, population dynamics, and maize production in Togo: implications for global warming and food security." Farming System 1.3 (2023): 100053.

Li, Xiaying, Belle Li, and Su-Je Cho. "Empowering Chinese Language Learners from Low-Income Families to Improve Their Chinese Writing with ChatGPT’s Assistance Afterschool." Languages 8.4 (2023): 238.

Cover

Downloads

Published

28-03-2024

How to Cite

[1]
P. L. Ramirez, “AI and Machine Learning in Healthcare Robotics: Exploring AI-driven algorithms and their role in addressing healthcare challenges, including task optimization and patient care”, J. Computational Intel. & Robotics, vol. 4, no. 1, pp. 76–85, Mar. 2024.

Issue

Vol. 4 No. 1 (2024): Journal of Computational Intelligence and Robotics

Section

Articles

License

Creative Commons License

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

License Terms

Ownership and Licensing:

Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.

License Permissions:

Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.

Additional Distribution Arrangements:

Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.

Online Posting:

Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.

Responsibility and Liability:

Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.