The Application of Artificial Intelligence in Diabetes Management

Siyu Jiang

doi:10.62051/ijphmr.v6n1.03

Authors

Siyu Jiang

DOI:

https://doi.org/10.62051/ijphmr.v6n1.03

Keywords:

Artificial Intelligence, Diabetes, Dietary Intervention, Personalized, Machine Learning, Deep Learning

Abstract

With the global prevalence of diabetes continuously rising, the management of diabetes has become a significant challenge in the field of public health. The application of Artificial Intelligence (AI) technologies in diabetes management, especially in personalized dietary interventions, has shown tremendous potential. This study conducts a systematic literature review to explore the various applications of AI in diabetes management, covering areas such as dietary intervention, personalized treatment, and complication prediction. The study finds that AI can effectively predict the risk of diabetes, optimize dietary plans, improve patient adherence, and reduce the incidence of diabetes-related complications. However, despite the significant progress made by AI technologies, challenges remain in terms of technological standardization, data privacy protection, and clinical translation. Future research should focus on the comprehensive application of AI in diabetes management, including combining exercise interventions, enhancing model accuracy, and expanding the diversity of datasets. Overall, the application of AI in personalized diabetes management holds great promise, but its clinical translation and long-term use still require further research and optimization.

References

[1] He, Y., & Liu, Y. (2024). Progress in the application of artificial intelligence in the clinical management of major chronic diseases. *Chongqing Medicine, 53*(24), 3681-3685+3691.

[2] Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., Yu, S., Wang, Z., Shu, J., Hou, X., Yang, X., Jia, W., & Sheng, B. (2023). Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. *Cell Reports Medicine, 4*(10), 101213. https://doi.org/10.1016/j.xcrm.2023.101213

[3] Huang, J., Yeung, A. M., Armstrong, D. G., Battarbee, A. N., Cuadros, J., Espinoza, J. C., Kleinberg, S., Mathioudakis, N., Swerdlow, M. A., & Klonoff, D. C. (2023). Artificial intelligence for predicting and diagnosing complications of diabetes. *Journal of Diabetes Science and Technology, 17*(1), 224-238. https://doi.org/10.1177/19322968221124583

[4] Zhu, Y. (2025). Research on hypoglycemic fear and primary care intervention measures in type 2 diabetes patients in the community. In *Proceedings of the Academic Symposium on Clinical Medical Innovation and Practice* (pp. 1375-1377). Chongqing Health Promotion and Health Education Society.

[5] Ming, Y., & Xiao, S. (2020). Practice and reflection on diabetes management among primary care nurses in the specialized disease care alliance model. *Journal of Taizhou Polytechnic, 25*(03), 82-85.

[6] Tarumi, S., Takeuchi, W., Chalkidis, G., Rodriguez-Loya, S., Kuwata, J., Flynn, M., Turner, K. M., Sakaguchi, F. H., Weir, C., Kramer, H., Shields, D. E., Warner, P. B., Kukhareva, P., Ban, H., &Kawamoto, K. (2021). Leveraging artificial intelligence to improve chronic disease care: Methods and application to pharmacotherapy decision support for type-2 diabetes mellitus. *Methods of Information in Medicine,60*(S01), e32-e43. https://doi.org/10.1055/s-0041-1728757

[7] Albert, L., Capel, I., Garcia-Saez, G., Martin-Redondo, P., Hernando, M. E., & Rigla, M. (2020). Managing gestational diabetes mellitus using a smartphone application with artificial intelligence (SineDie) during the COVID-19 pandemic: Much more than just telemedicine. *Diabetes Research and Clinical Practice, 169*, 108396. https://doi.org/10.1016/j.diabres.2020.108396

[8] Wang, X., Sun, Z., Xue, H., & An, R. (2025). Artificial intelligence applications to personalized dietary recommendations: A systematic review. *Healthcare (Basel), 13*(12), 1417. https://doi.org/10.3390/healthcare13121417

[9] Huang, J., Fang, J., & Lin, J. (2025). Artificial intelligence in nutritional support therapy for critical care. *Chinese Journal of Integrated Traditional Chinese and Western Medicine, 32*(2), 238-241. https://doi.org/10.3969/j.issn.1008-9691.2025.02.023

[10] Li, Y., Tu, W., Yin, T., et al. (2020). A review of the scope of application of artificial intelligence in nutritional management of patients with inflammatory bowel disease. *Chinese General Practice, 28*(14), 1709-1716. https://doi.org/10.12114/j.issn.1007-9572.2024.0276

[11] Zhang, P. (2020). Model and AI-oriented formative study on dietary intervention for hypertensive patients. *Anhui Medical University*.

[12] Shu, Q., Pang, J., Liu, Z., Liang, X., Chen, M., Tao, Z., Liu, Q., Guo, Y., Yang, X., Ding, J., Chen, R., Wang, S., Li, W., Zhai, G., Xu, J., & Li, L. (2024). Artificial intelligence for early detection of pediatric eye diseases using mobile photos. *JAMA Network Open, 7*(8), e2425124. https://doi.org/10.1001/jamanetworkopen.2024.25124

[13] Stringer, J. S. A., Pokaprakarn, T., Prieto, J. C., Vwalika, B., Chari, S. V., Sindano, N., Freeman, B. L., Sikapande, B., Davis, N. M., Sebastiao, Y. V., Mandona, N. M., Stringer, E. M., Benabdelkader, C., Mungole, M., Kapilya, F. M., Almnini, N., Diaz, A. N., Fecteau, B. A., Kosorok, M. R., Cole, S. R., & Kasaro, M. P. (2024). Diagnostic accuracy of an integrated AI tool to estimate gestational age from blind ultrasound sweeps. *JAMA, 332*(8), 649-657. https://doi.org/10.1001/jama.2024.10770

[14] Pacchiano, F., Tortora, M., Criscuolo, S., Jaber, K., Acierno, P., De Simone, M., Tortora, F., Briganti, F., & Caranci, F. (2024). Artificial intelligence applied in acute ischemic stroke: From child to elderly. *Radiology and Medicine, 129*(1), 83-92. https://doi.org/10.1007/s11547-023-01735-1

[15] Shiwani, T., Relton, S., Evans, R., Kale, A., Heaven, A., Clegg, A., Ageing Data Research Collaborative (Geridata) AI group, & Todd, O. (2023). New horizons in artificial intelligence in the healthcare of older people. *Age and Ageing, 52*(12), afad219. https://doi.org/10.1093/ageing/afad219

[16] Naidoo, J., Shelmerdine, S. C., Charcape, C. F. U., & Sodhi, A. S. (2023). Artificial intelligence in pediatric tuberculosis. *Pediatric Radiology, 53*(9), 1733-1745. https://doi.org/10.1007/s00247-023-05606-9

[17] Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Cheng, Y., Vedadi, E., Tomasev, N., Azizi, S., Singhal, K., Hou, L., Webson, A., Kulkarni, K., Mahdavi, S. S., Semturs, C., Gottweis, J., Barral, J., Chou, K., Corrado, G. S., Matias, Y., Karthikesalingam, A., & Natarajan, V. (2025). Towards conversational diagnostic artificial intelligence. *Nature, 642*(8067), 442-450. https://doi.org/10.1038/s41586-025-08866-7

[18] Wu, Y., Min, H., Li, M., Shi, Y., Ma, A., Han, Y., Gan, Y., Guo, X., & Sun, X. (2023). Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: Protocol for a mixed-methods study. *BMC Public Health, 23*(1), 1325. https://doi.org/10.1186/s12889-023-16066-z

[19] Li, R., Wang, S., & Wu, T. (2025). Research on the impact of AI-based mobile health services on self-management behaviors of diabetic patients. *Health Soft Science, 33*(5), 6-53. https://doi.org/10.3969/j.issn.1008-9993.2019.11.007

[20] Chen, M. H., Wang, Z. A., Li, J. Y., et al. (2025). Based on artificial intelligence auxiliary classification of diabetic retinopathy study. *Laser and Optoelectronics, 1*-21. https://doi.org/10.3788/LOP251367

[21] Xiang, J. (2025). Application of machine learning in the prediction of diabetes. *Journal of Information Science and Technology of China, (14)*, 77-80. https://doi.org/10.3969/j.issn.1008-9691.2025.02.023

[22] Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. *Journal of Medical Internet Research, 21*(5), e13216. https://doi.org/10.2196/13216

[23] Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Cheng, Y., Vedadi, E., Tomasev, N., Azizi, S., Singhal, K., Hou, L., Webson, A., Kulkarni, K., Mahdavi, S. S., Semturs, C., Gottweis, J., Barral, J., Chou, K., Corrado, G. S., Matias, Y., Karthikesalingam, A., & Natarajan, V. (2025). Towards conversational diagnostic artificial intelligence. *Nature, 642*(8067), 442-450. https://doi.org/10.1038/s41586-025-08866-7

[24] Gao, G. (2024). Discussion on issues related to the application of conversational AI in psychotherapy. *Medicine and Philosophy, (23)*, 61-66. https://doi.org/10.3969/j.issn.1008-9691.2025.02.023

[25] Liu, L., & Chen, M. (2022). Evaluation of the application effect of remote psychological behavior intervention guided by health belief model and artificial intelligence in gastric cancer patients. *Nursing Practice and Research, (8)*, 1181-1185. https://doi.org/10.3969/j.issn.1008-9691.2025.02.023

[26] Ai, M., Sun, W., Ding, Q., et al. (2019). The effect of APP-based diet diary on diet control in patients with impaired glucose tolerance in prediabetes. *Chinese Journal of Nursing, 36*(11), 27-30. https://doi.org/10.3969/j.issn.1008-9693.2019.11.007

[27] Nomura, A., Noguchi, M., Kometani, M., Furukawa, K., & Yoneda, T. (2021). Artificial intelligence in current diabetes management and prediction. *Current Diabetes Reports, 21*(12), 61. https://doi.org/10.1007/s11892-021-01423-2

[28] Mackenzie, S. C., Sainsbury, C. A. R., & Wake, D. J. (2024). Diabetes and artificial intelligence beyond the closed loop: A review of the landscape, promise, and challenges. *Diabetologia, 67*(2), 223-235. https://doi.org/10.1007/s00125-023-06038-8