A Swarm-Based Clinical Validation Framework of Artificial Intelligence Solutions for Non-Communicable Diseases
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, Revised: 27 August 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.
- World Health Organization. “Noncommunicable diseases”, 2021, Retrieved from https://www.who.int/health-topics/noncommunicable-diseases#tab=tab_1
- M. Marmot, M, “Health equity in England: The Marmot Review 10 Years on”, BMJ, vol. 693, no. 1, pp. 1-20, 2020, doi:10.1136/bmj.m693
- F. Luna, V.A. Luyckx, “Why have Non-communicable Diseases been Left Behind?” Asian Bioeth Rev, Vol. 12, no.1, pp. 5-25, 2020, doi: 10.1007/s41649-020-00112-8
- N.T. Castillo-Carandang, R.D. Buenaventura, Y.C. Chia, D. Do Van, C. Lee, N.L. Duong, et al., “Moving Towards Optimized Noncommunicable Disease Management in the ASEAN Region: Recommendations from a Review and Multidisciplinary Expert Panel”, Risk Manag Healthc Policy, Vol. 13, no. 1, pp. 803-819, 2020, doi: 10.2147/RMHP.S256165. PMID: 32765135; PMCID: PMC7371561.
- S. Xiong, H. Lu, N. Peoples, et al., “Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries”, npj Digit Med, Vol. 6, no. 12, pp. 1-20, 2023, doi: https://doi.org/10.1038/s41746-023-00764-4
- Z. Obermeyer, E.J. Emanuel, L.O. Gostin, “Big data, big responsibilities: A paradox of information in the era of artificial intelligence”, JAMA, Vol. 316, no. 6, pp. 601–602, 2016.
- Α. Rajkomar, J. Dean, I. Kohane, “Machine learning in medicine”, New England Journal of Medicine, Vol. 380, no. 14, pp. 1347-1358, 2018, doi: 10.1056/NEJMra1814259.
- E.J. Topol, “High-performance medicine: The convergence of human and artificial intelligence”, Nature Medicine, Vol. 25, no. 1, pp. 44-56, 2019, doi: 10.1038/s41591-018-0300-7.
- K. Denecke, “Artificial intelligence and medical informatics: State of the art and challenges for the future”, Yearbook of Medical Informatics, Vol. 28, no. 1, pp. 164-166, 2019, doi: 10.1055/s-0039-1677902.
- X. Liu, L. Faes, A.U, Kale, S.K. Wagner, D. Fu, et al., “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis”, The Lancet Digital Health, Vol. 2, no. 6, pp. 271-297, 2020, doi: 10.1016/S2589-7500(20)30034-7.
- G. Hinton, O. Vinyals, J. Dean, Distilling the knowledge in a neural network. 2019, arXiv preprint arXiv:1503.02531.
- G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, “Densely connected convolutional networks”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2018.
- R.S. Parpinelli, T.O. Sousa, P.E. Miyagi, “Particle swarm optimization applied to the clinical validation of artificial intelligence models for breast cancer detection”, Expert Systems with Applications, Vol.155, no. 1, pp. 113-134, 113434, 2020.
- W. Dai, Y, Ma, Y. Xiong, Y. Li, Y., H. Zhang, X. Zhang, “A swarm intelligence-based clinical validation approach for cardiovascular disease risk prediction using deep learning models”, Computers in Biology and Medicine, Vol. 137, no 1, pp. 1-24, 2021, doi: 10.1016/j.compbiomed.2021.104819.
- H.D. Nguyen, M.D. Nguyen, T.H. Tran, D.A. Duong, “A swarm-based interpretability framework for AI models in diabetic retinopathy diagnosis”, Expert Systems with Applications, Vol: 189, no. 1, pp. 115-134, 2021.
- J. Kennedy, R. Eberhart, R. Swarm Intelligence. Morgan Kaufmann, 2001.
- E. Bonabeau, M. Dorigo, G. Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems”, Oxford University Press, 1999.
- G. Hinton, et al., “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups”, IEEE Signal Processing Magazine, Vol. 29, no. 6, pp. 82-97, 2015, doi: 10.1109/MSP.2012.2205597.
- A. Esteva, et al., “Dermatologist-level classification of skin cancer with deep neural networks” Nature, Vol. 542, no. 7639, pp. 115-118, 2017, doi: 10.1038/nature21056.
- J. Lee, et al., “Machine learning in medical imaging: A systematic review of the literature and potential applications for emergency radiology”, Emergency Radiology, Vol. 27, no. 6, pp. 669-679, 2020, doi: 10.1007/s10140-020-01860-w.
- A. Rajkomar, et al., “Scalable and accurate deep learning with electronic health records”, npj Digital Medicine, Vol. 1, no. 1, pp. 18-24, 2018, doi: 10.1038/s41746-018-0029-1.
- A.L. Beam, et al., “Big data and machine learning in health care”, JAMA, Vol. 322, no. 13, pp. 1317-1318, 2020, doi: 10.1001/jama.2019.21445.
- Z. Obermeyer, et al., “Dissecting racial bias in an algorithm used to manage the health of populations”, Science, Vol. 366, no. 6464, pp. 447-453, 2019, doi: 10.1126/science.aax2342.
- F.S. Collins, et al., “A new initiative on precision medicine”, New England Journal of Medicine, Vol. 372, no. 9, pp. 793-795, 2020, doi: 10.1056/NEJMp1500523.
- K.H. Yu, et al., “Artificial intelligence in healthcare”, Nature Biomedical Engineering, Vol. 2, no. 10, pp. 719-731., 2018, doi: 10.1038/s41551-018-0305-z.
