by Kitty Kioskli 1,2 * , Spyridon Papastergiou 3,4, Theofanis Fotis 5
1 University of Essex, School of Computer Science and Electronic Engineering, Institute of Analytics and Data Science (IADS), Essex, United Kingdom
2 trustilio B.V., Amsterdam, Netherlands
3 University of Piraeus, Department of Informatics, Piraeus, Greece
4 Security Labs Consulting Limited, Cork, Ireland
5 University of Brighton, School of Sport & Health Sciences, Centre for Secure, Intelligent and Usable Systems (CSIUS), Brighton, United Kingdom
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 2, Issue 9, Page # 1-11, 2023; DOI: 10.55708/js0209001
Keywords: Swarm Intelligence, Clinical Validation, Framework, Non-Communicable Diseases, Diagnostic Accuracy, Personalized Treatment
Received: 28 January 2023, Accepted: 28 August 2023, Published Online: 26 September 2023
APA Style
Kioskli, K., Fotis, T., & Papastergiou, S. (2023). A Swarm-Based Clinical Validation Framework of Artificial Intelligence Solutions for Non-Communicable Diseases. Journal of Engineering Research and Sciences, 2(9), 1–11. https://doi.org/10.55708/js0209001
Chicago/Turabian Style
Kioskli, Kitty, Theofanis Fotis, and Spyridon Papastergiou. 2023. “A Swarm-Based Clinical Validation Framework of Artificial Intelligence Solutions for Non-Communicable Diseases.” Journal of Engineering Research and Sciences 2 (9): 1–11. doi:10.55708/js0209001.
IEEE Style
[1] K. Kioskli, T. Fotis, and S. Papastergiou, “A Swarm-Based Clinical Validation Framework of Artificial Intelligence Solutions for Non-Communicable Diseases,” Journal of Engineering Research and Sciences, vol. 2, no. 9, pp. 1–11, 2023. doi:10.55708/js0209001
Non-communicable diseases (NCDs) present complex challenges in patient care. Artificial Intelligence (AI) offers transformative potential, but its implementation requires addressing key issues. This study proposes a swarm intelligence-inspired clinical validation framework for NCDs, promoting openness, trustworthiness, and continuous self-validation. The framework creates a collaborative environment, connecting healthcare entities, patients, caregivers, and professionals. The swarm-based approach enhances diagnostic accuracy, enables personalized treatment, improves prognosis, supports clinical decision-making, engages patients, enables real-time monitoring, and promotes continuous learning. These implications have the power to revolutionize NCD management and improve patient outcomes.
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