ARCHIVES
Original Article
Opportunities and Challenges of AI-Based Diabetes Prediction: Global and Regional Perspectives
Ramesh Prasad Bhatta1
Assistant Professor, Central Department of CSIT, Far Western University, Mahendranagar, Nepal.
Published Online: January-April 2026
Pages: 107-113
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501015References
1. Oxford Dictionaries, Artificial intelligence. Oxford Dictionaries | English. Available:
https://en.oxforddictionaries.com/definition/artificial_intelligence
2. International Diabetes Federation, IDF Diabetes Atlas, 10th ed. Brussels, Belgium: IDF, 2021. Available: https://www.diabetesatlas.org
3. P. K. et al., “Diabetes and hypertension in the SAARC region: A systematic review and meta-analysis,” Journal of Clinical Hypertension,
vol. 23, no. 5, pp. 1051–1061, May 2021, doi: 10.1111/jch.14235.
4. I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine learning and data mining methods in diabetes
research,” Computational and Structural Biotechnology Journal, vol. 15, pp. 104–116, 2017.
5. L. H. Messer et al., “Optimizing hybrid closed-loop therapy in adolescents and emerging adults using the MiniMed 670G system,” Diabetes
Care, vol. 41, no. 4, pp. 789–796, 2018.
6. R. Singla, A. Singla, Y. Gupta, and S. Kalra, “Artificial intelligence/machine learning in diabetes care,” Indian Journal of Endocrinology and
Metabolism, vol. 23, no. 5, pp. 495–497, 2019.
7. S. Ziajor et al., “The use of artificial intelligence in the diagnosis and detection of complications of diabetes,” Journal of Education, Health
and Sport, vol. 65, pp. 11–27, 2024, doi: 10.12775/jehs.2024.65.001.
8. T. Zhu et al., “Deep learning for diabetes: A systematic review,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 7, pp.
2744–2757, 2020, doi: 10.1109/JBHI.2020.3040225.
9. G. Fagherazzi and P. Ravaud, “Digital diabetes: Perspectives for diabetes prevention, management and research,” Diabetes & Me tabolism,
vol. 45, no. 4, pp. 322–329, 2019, doi: 10.1016/j.diabet.2018.08.012.
10. B. Mesko, “The role of artificial intelligence in precision medicine,” Expert Review of Precision Medicine and Drug Development, vol. 2,
no. 5, pp. 239–241, 2017, doi: 10.1080/23808993.2017.1380516.
11. G. N. Graham, M. Ostrowski, and A. B. Sabina, “Population health-based approaches to utilizing digital technology: A strategy for equity,”
Journal of Public Health Policy, vol. 37, pp. 154–166, 2016, doi: 10.1057/s41271-016-0012-5.
12. B. E. Himes and E. R. Weitzman, “Innovations in health information technologies for chronic pulmonary diseases,” Respiratory Research,
vol. 17, p. 38, 2016, doi: 10.1186/s12931-016-0354-3.
13. C. N. Johnson-Mann, T. J. Loftus, and A. Bihorac, “Equity and artificial intelligence in surgical care,” JAMA Surgery, vol. 156, no. 6, pp.
509–510, 2021, doi: 10.1001/jamasurg.2020.7208.
14. R. Schnall et al., “A user-centered model for designing consumer mobile health (mHealth) applications,” Journal of Biomedical Informatics,
vol. 60, pp. 243–251, 2016, doi: 10.1016/j.jbi.2016.02.002.
15. C. W. Carspecken et al., “A clinical case of electronic health record drug alert fatigue: Consequences for patient outcome,” Pediatrics, vol.
131, no. 6, pp. e1970–e1973, 2013, doi: 10.1542/peds.2012-3252.
16. J. S. Ash, M. Berg, and E. Coiera, “Some unintended consequences of information technology in health care: The nature of patient care
information system-related errors,” Journal of the American Medical Informatics Association, vol. 11, no. 2, pp. 104–112, 2004, doi:
10.1197/jamia.M1471.
17. 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.
https://en.oxforddictionaries.com/definition/artificial_intelligence
2. International Diabetes Federation, IDF Diabetes Atlas, 10th ed. Brussels, Belgium: IDF, 2021. Available: https://www.diabetesatlas.org
3. P. K. et al., “Diabetes and hypertension in the SAARC region: A systematic review and meta-analysis,” Journal of Clinical Hypertension,
vol. 23, no. 5, pp. 1051–1061, May 2021, doi: 10.1111/jch.14235.
4. I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine learning and data mining methods in diabetes
research,” Computational and Structural Biotechnology Journal, vol. 15, pp. 104–116, 2017.
5. L. H. Messer et al., “Optimizing hybrid closed-loop therapy in adolescents and emerging adults using the MiniMed 670G system,” Diabetes
Care, vol. 41, no. 4, pp. 789–796, 2018.
6. R. Singla, A. Singla, Y. Gupta, and S. Kalra, “Artificial intelligence/machine learning in diabetes care,” Indian Journal of Endocrinology and
Metabolism, vol. 23, no. 5, pp. 495–497, 2019.
7. S. Ziajor et al., “The use of artificial intelligence in the diagnosis and detection of complications of diabetes,” Journal of Education, Health
and Sport, vol. 65, pp. 11–27, 2024, doi: 10.12775/jehs.2024.65.001.
8. T. Zhu et al., “Deep learning for diabetes: A systematic review,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 7, pp.
2744–2757, 2020, doi: 10.1109/JBHI.2020.3040225.
9. G. Fagherazzi and P. Ravaud, “Digital diabetes: Perspectives for diabetes prevention, management and research,” Diabetes & Me tabolism,
vol. 45, no. 4, pp. 322–329, 2019, doi: 10.1016/j.diabet.2018.08.012.
10. B. Mesko, “The role of artificial intelligence in precision medicine,” Expert Review of Precision Medicine and Drug Development, vol. 2,
no. 5, pp. 239–241, 2017, doi: 10.1080/23808993.2017.1380516.
11. G. N. Graham, M. Ostrowski, and A. B. Sabina, “Population health-based approaches to utilizing digital technology: A strategy for equity,”
Journal of Public Health Policy, vol. 37, pp. 154–166, 2016, doi: 10.1057/s41271-016-0012-5.
12. B. E. Himes and E. R. Weitzman, “Innovations in health information technologies for chronic pulmonary diseases,” Respiratory Research,
vol. 17, p. 38, 2016, doi: 10.1186/s12931-016-0354-3.
13. C. N. Johnson-Mann, T. J. Loftus, and A. Bihorac, “Equity and artificial intelligence in surgical care,” JAMA Surgery, vol. 156, no. 6, pp.
509–510, 2021, doi: 10.1001/jamasurg.2020.7208.
14. R. Schnall et al., “A user-centered model for designing consumer mobile health (mHealth) applications,” Journal of Biomedical Informatics,
vol. 60, pp. 243–251, 2016, doi: 10.1016/j.jbi.2016.02.002.
15. C. W. Carspecken et al., “A clinical case of electronic health record drug alert fatigue: Consequences for patient outcome,” Pediatrics, vol.
131, no. 6, pp. e1970–e1973, 2013, doi: 10.1542/peds.2012-3252.
16. J. S. Ash, M. Berg, and E. Coiera, “Some unintended consequences of information technology in health care: The nature of patient care
information system-related errors,” Journal of the American Medical Informatics Association, vol. 11, no. 2, pp. 104–112, 2004, doi:
10.1197/jamia.M1471.
17. 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.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026