A New Era in Diabetes Management: Generative Artificial Intelligence

Meleknur Göktaş; Tuğba Bilgehan

A New Era in Diabetes Management: Generative Artificial Intelligence

Abstract

Diabetes mellitus (DM) is a rapidly increasing global health issue that requires effective selfmanagement to prevent complications and improve quality of life. In recent years, advancements in generative artificial intelligence (GenAI) have created new opportunities to support DM selfmanagement by providing personalized care solutions. This study is designed as a systematic review. Numerous studies in the literature have examined the contributions of GenAI models to DM self-management, and reviewing these studies is essential to provide a general framework on this topic. The primary aim of this study is to systematically examine research that utilizes GenAI in DM management. This systematic review was conducted in accordance with PRISMA guidelines. A comprehensive literature search was carried out between February and October 2024 across PubMed, Scopus, Web of Science, Google Scholar, Ulakbim, Türk Medline, and national databases. Using the keywords "diabetes," "generative artificial intelligence," and "diabetes self-management," studies published between 2018 and 2024 were identified. A total of 19 studies that met the inclusion criteria were analyzed in terms of the GenAI models used, application areas, and reported outcomes. Among the reviewed studies, GPT-based models were predominant, appearing in 53% of the research. In addition, models such as GAN, LSTM, WaveNet, GRU, Markov-Bayes, Google Bard, and Mobiguide were also utilized. Moreover, the findings of this study highlight that GenAI-based systems are widely adopted in DM selfmanagement and possess significant potential to facilitate this process. These systems not only provide information but also incorporate advanced support mechanisms that enhance patient monitoring and clinical decision-making processes. GenAI has made notable contributions to DM care, particularly by developing personalized care plans, offering tailored dietary and exercise recommendations, generating educational materials, predicting blood glucose (BG) levels, providing individualized guidance, and supporting clinical workflows. As GenAI continues to evolve and adapt to the specific contexts and demands of the medical field, its role in DM care is expected to become increasingly prominent. However, several challenges have been reported, including concerns over data security, privacy, misinformation generation, and suboptimal performance in detecting critical conditions such as hypoglycemia. Addressing these ethical, technical, and security-related limitations requires further research and technological advancements. Future studies should prioritize enhancing the reliability, usability, and diagnostic accuracy of GenAI applications to ensure their seamless integration into clinical practice.

Keywords

References

[1] The Society of Endocrinology and Metabolism of Turkey. (2024). Guidelines for the diagnosis, treatment, and monitoring of diabetes mellitus and its complications-2024. https://file.temd.org.tr/Uploads/publications/guides/documents/diabetesmellitus2024.pdf
[2] Republic of Türkiye Ministry of Health. (2023). Türkiye diabetes program 2023 – 2027. https://hsgm.saglik.gov.tr/depo/birimler/saglikli-beslenme-ve-hareketli-hayat-db/Dokumanlar/Programlar/Turkiye-Diyabet-Programi.pdf
[3] Magliano, D. J., Boyko, E. J. IDF Diabetes Atlas 10th edition scientific committee. (2021). Idf diabetes atlas. (10th ed.). International Diabetes Federation. https://pubmed.ncbi.nlm.nih.gov/35914061/
[4] World Health Organization. (2003). Adherence to long-term therapies: evidence for action. https://iris.who.int/handle/10665/42682
[5] American Diabetes Association. (2023). Standards of care in diabetes-2023. https://www.portailvasculaire.fr/sites/default/files/docs/2023_ada_diabete_standards_of_care_in_diabetes_diab_care.pdf
[6] Harding, J. L., Pavkov, M. E., Magliano, D. J., Shaw, J. E., & Gregg, E. W. (2019). Global trends in diabetes complications: a review of current evidence. Diabetologia, 62, 3-16. https://doi.org/10.1007/s00125-018-4711-2
[7] American Diabetes Association. (2024). 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024. Diabetes Care, 47(1), 20-42. https://doi.org/10.2337/dc24-S002
[8] American Association of Diabetes Educators. (2009). AADE Guidelines for the Practice of Diabetes Self-Management Education and Training (DSME/T). The Diabetes Educator, 35(3), 85-107. https://doi.org/10.1177/0145721709352436

[9] Williams, D. M., Jones, H., & Stephens, J. W. (2022). Personalized type 2 diabetes management: an update on recent advances and recommendations. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 281-295. https://doi.org/10.2147/DMSO.S331654
[10] Khor, X. Y., Pappachan, J. M., & Jeeyavudeen, M. S. (2023). Individualized diabetes care: Lessons from the real-world experience. World Journal of Clinical Cases, 11(13), 2890. https://doi.org/10.12998/wjcc.v11.i13.2890
[11] Song, M., Choe, M. A., Kim, K. S., Yi, M. S., Lee, I., Kim, J., Lee, M., Cho, M.Y., & Shim, Y. S. (2009). An evaluation of Web‐based education as an alternative to group lectures for diabetes self‐management. Nursing & health sciences, 11(3), 277-284. https://doi.org/10.1111/j.1442-2018.2009.00458.x
[12] Hunt, C. W. (2015). Technology and diabetes self-management: An integrative review. World journal of diabetes, 6(2), 225–233. https://doi.org/10.4239/wjd.v6.i2.225
[13] Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q. H., Lim, Z. W., Yew, S. M. E., Goh, J. H. L., Bee, Y. M., Sabanayagam, C., Sevdalis, N., Lim, C. C., Lim, C. T., Shaw, J., Jia, W., Ekinci, E. I., Simó, R., Lim, L. L., Li, H., & Tham, Y. C. (2024). Artificial intelligence for diabetes care: current and future prospects. The Lancet. Diabetes & Endocrinology, 12(8), 569–595. https://doi.org/10.1016/S2213-8587(24)00154-2
[14] Goel, A.K., & Davies, J. (2020). Artificial Intelligence. In R. J. Sternberg (Ed.), The Cambridge Handbook of Intelligence (602-625). Cambridge: Cambridge University Press.
[15] Cahn, A., Akirov, A., & Raz, I. (2018). Digital health technology and diabetes management. Journal of diabetes, 10(1), 10–17. https://doi.org/10.1111/1753-0407.12606
[16] Aggarwal, M., & Murty, M. N. (2021). Deep learning. Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs (35-66). Springer Singapore. https://doi.org/10.1007/978-981-33-4022-0_3
[17] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4). https://doi.org/10.1136/svn-2017-000101
[18] García-Peñalvo, F. J., & Vázquez-Ingelmo, A. (2023). What do we mean by GenAI? A systematic mapping of the evolution, trends, and techniques involved in Generative AI. International Journal of Interactive Multimedia and Artificial Intelligence, 8(4), 7-15. https://doi.org/10.9781/ijimai.2023.07.006
[19] Liu, S., Chen, J., Feng, Y., Xie, Z., Pan, T., & Xie, J. (2024). Generative artificial intelligence and data augmentation for prognostic and health management: Taxonomy, progress, and prospects. Expert Systems with Applications, 255, 124511. https://doi.org/10.1016/j.eswa.2024.124511
[20] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
[21] Hernandez-Matamoros, A., Fujita, H., & Perez-Meana, H. (2020). A novel approach to create synthetic biomedical signals using BiRNN. Information Sciences, 541, 218-241. https://doi.org/10.1016/j.ins.2020.06.019
[22] Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136. https://doi.org/10.1109/JAS.2023.123618
[23] Balasubramaniam, S., Chirchi, V., Kadry, S., Agoramoorthy, M., Gururama, S. P., Satheesh, K. K., & Sivakumar, T. A. (2024). The road ahead: Emerging trends, unresolved issues, and concluding remarks in generative AI—A comprehensive review. International Journal of Intelligent Systems, 2024. https://doi.org/10.1155/2024/4013195
[24] Howell, M. D. (2024). Generative artificial intelligence, patient safety and healthcare quality: a review. BMJ Quality & Safety, 33(11), 748-754. https://doi.org/10.1136/bmjqs-2023-016690
[25] Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022). Multimodal machine learning in precision health: A scoping review. npj Digital Medicine, 5(1), 171. https://doi.org/10.1038/s41746-022-00712-8
[26] Yang, K., Ji, S., Zhang, T., Xie, Q., & Ananiadou, S. (2023). On the evaluations of chatgpt and emotion-enhanced prompting for mental health analysis. arXiv preprint
[27] Reddy, S. (2024). Generative AI in healthcare: An implementation science informed translational path on application, integration and governance. Implementation Science, 19(1), 27. https://doi.org/10.1186/s13012-024-01357-9
[28] Gabrielson, A. T., Odisho, A. Y., & Canes, D. (2023). Harnessing generative artificial intelligence to improve efficiency among urologists: welcome ChatGPT. Journal of Urology, 209(5), 827-829.
[29] Hiba, A., Al-Qudheeby, M., Kheyami, Z. A., Khalil, R., Khamees, N., Hijjawi, O., Sallam, M., & Barakat, M. (2024). Cross-linguistic evaluation of generative AI models for diabetes and endocrine queries. Jordan Medical Journal, 58(4). https://doi.org/10.35516/jmj.v58i4.3369
[30] Ayre, J., Mac, O., McCaffery, K., McKay, B. R., Liu, M., Shi, Y., Rezvan, A., & Dunn, A. G. (2024). New frontiers in health literacy: using ChatGPT to simplify health information for people in the community. Journal of General Internal Medicine, 39(4), 573-577. https://doi.org/10.1007/s11606-023-08469-w
[31] Chen, A., Chen, D. O., & Tian, L. (2024). Benchmarking the symptom-checking capabilities of ChatGPT for a broad range of diseases. Journal of the American Medical Informatics Association, 31(9), 2084-2088. https://doi.org/10.1093/jamia/ocad245
[32] Huang, R. S. T., Lu, K. J. Q., Meaney, C., Kemppainen, J., Punnett, A., & Leung, F. H. (2023). Assessment of resident and AI chatbot performance on the University of Toronto family medicine residency progress test: comparative study. JMIR Medical Education, 9, e50514. https://doi.org/10.2196/50514
[33] Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., & Smith, D. M. (2023). Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA internal medicine, 183(6), 589-596. https://doi.org/10.1001/jamainternmed.2023.1838
[34] Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group. (2009). Reprint—preferred reporting items for systematic reviews and metaanalyses: The PRISMA Statement. Physical Therapy, 89(9), 873-880.
[35] Dudukcu, H. V., Taskiran, M., & Yildirim, T. (2021). Blood glucose prediction with deep neural networks using weighted decision level fusion. Biocybernetics and Biomedical Engineering, 41(3), 1208-1223. https://doi.org/10.1016/j.bbe.2021.08.007
[36] Zhu, T., Yao, X., Li, K., Herrero, P., & Georgiou, P. (2020). Blood glucose prediction for type 1 diabetes using generative adversarial networks. In CEUR workshop proceedings (Vol. 2675, pp. 90-94).
[37] Wang, X., Lin, Y., Xiong, Y., Zhang, S., He, Y., He, Y., Plasek, J. M., Zhou, L., Bates, D. W., & Tang, C. (2022). Using an optimized generative model to infer the progression of complications in type 2 diabetes patients. BMC Medical Informatics and Decision Making, 22(1), 174.
[38] Hernandez, C. A., Gonzalez, A. E. V., Polianovskaia, A., Sanchez, R. A., Arce, V. M., Mustafa, A., Vypritskaya, E., Gutierrez, O. P., Bashir, M., & Sedeh, A. E. (2023). The future of patient education: AI-driven guide for type 2 diabetes. Cureus, 15(11).
[39] Sun, H., Zhang, K., Lan, W., Gu, Q., Jiang, G., Yang, X., Qin, W., & Han, D. (2023). An AI dietitian for type 2 diabetes mellitus management based on large language and image recognition models: preclinical concept validation study. Journal of medical Internet research, 25, e51300.
[40] Mashatian, S., Armstrong, D. G., Ritter, A., Robbins, J., Aziz, S., Alenabi, I., Huo, M., Anand, T., & Tavakolian, K. (2024). Building trustworthy generative artificial intelligence for diabetes care and limb preservation: a medical knowledge extraction case. Journal of Diabetes Science and Technology, 19322968241253568. https://doi.org/10.1177/19322968241253568
[41] Lee, H. E., Jun Woo, C. H. O. I., & Kang, M. S. (2024). A Study on the development of a chatbot using generative AI to provide diets for diabetic patients. Korean Journal of Artificial Intelligence, 12(3), 25-31. https://doi.org/10.24225/kjai.2024.12.3.25
[42] Posa, K. N., Peralta, G. E. Q., Hidalgo, I. F., Prat-Oriol, B., & Abreu, R. (2024). Comparative evaluation of generative artificial intelligence systems for patient queries on age-related macular degeneration and diabetic macular edema. Investigative Ophthalmology & Visual Science, 65(7), 2326-2326.
[43] Naja, F., Taktouk, M., Matbouli, D., Khaleel, S., Maher, A., Uzun, B., Alameddine, M., & Nasreddine, L. (2024). Artificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome. European Journal of Clinical Nutrition, 78(10), 887-896. https://doi.org/10.1038/s41430-024-01476-y
[44] Shiraishi, M., Lee, H., Kanayama, K., Moriwaki, Y., & Okazaki, M. (2024). Appropriateness of artificial intelligence chatbots in diabetic foot ulcer management. The international journal of lower extremity wounds, 15347346241236811. https://doi.org/10.1177/15347346241236811
[45] Chung, S. M., & Chang, M. C. (2024). Assessment of the information provided by ChatGPT regarding exercise for patients with type 2 diabetes: a pilot study. BMJ Health & Care Informatics, 31(1), e101006. https://doi.org/10.1136/bmjhci-2023-101006
[46] Gokbulut, P., Kuskonmaz, S. M., Onder, C. E., Taskaldiran, I., & Koc, G. (2024). Evaluation of ChatGPT-4 performance in answering patients' questions about the management of type 2 diabetes. Medical Bulletin of Sisli Etfal Hospital, 58(4). https://doi.org/10.14744/SEMB.2024.23697
[47] Kopitar, L., Fister Jr, I., & Stiglic, G. (2024). Using generative AI to improve the performance and interpretability of rule-based diagnosis of type 2 diabetes mellitus. Information, 15(3), 162. https://doi.org/10.3390/info15030162
[48] Peleg, M., Shahar, Y., & Quaglini, S. (2022). MobiGuide: guiding clinicians and chronic patients anytime, anywhere. Communications of the ACM, 65(4), 74-79. https://doi.org/10.1145/3511596
[49] Nayak, A., Vakili, S., Nayak, K., Nikolov, M., Chiu, M., Sosseinheimer, P., Talamentes, S., Testa, S., Palanisamy, S., Giri, V., Schulman, K., & Schulman, K. (2023). Use of voice-based conversational artificial intelligence for basal insulin prescription management among patients with type 2 diabetes: a randomized clinical trial. JAMA network open, 6(12), e2340232-e2340232. doi:10.1001/jamanetworkopen.2023.40232
[50] Shaikh, A., Baluni, S., Malpani, N., Lodha, P., & Meena, A. (2024). Decoding type 2 diabetes through point-of-care testing, cloud-based monitoring, and generative augmented retrieval model-driven virtual diabetes education: A comprehensive approach to glycemic control. International Journal of Diabetes and Technology, 3(1), 25-31. https://doi.org/10.4103/ijdt.ijdt_5_24
[51] Dey, A. K. (2023). ChatGPT in diabetes care: An overview of the evolution and potential of generative artificial intelligence model like ChatGPT in augmenting clinical and patient outcomes in the management of diabetes. International Journal of Diabetes and Technology, 2(2), 66-72. https://doi.org/10.4103/ijdt.ijdt_31_23
[52] Khan, S. (2023). The Future of Diabetes Care: Navigating with generative language models. Indus Journal of Medical and Health Sciences, 1(1), 109-116. https://induspublishers.com/IJMHS/article/view/58
[53] Sng, G. G. R., Tung, J. Y. M., Lim, D. Y. Z., & Bee, Y. M. (2023). Potential and pitfalls of ChatGPT and natural-language artificial intelligence models for diabetes education. Diabetes Care, 46(5), 103-105. https://doi.org/10.2337/dc23-0197
[54] Thyde, D. N., Mohebbi, A., Bengtsson, H., Jensen, M. L., & Mørup, M. (2021). Machine learning-based adherence detection of type 2 diabetes patients on once-daily basal insulin injections. Journal of diabetes science and technology, 15(1), 98-108. https://doi.org/10.1177/1932296820912411
[55] Cordeiro, R., Karimian, N., & Park, Y. (2021). Hyperglycemia identification using ECG in deep learning era. Sensors, 21(18), 6263. https://doi.org/10.3390/s21186263
[56] Megahed, F. M., Chen, Y. J., Ferris, J. A., Knoth, S., & Jones-Farmer, L. A. (2024). How generative AI models such as ChatGPT can be (mis) used in SPC practice, education, and research? An exploratory study. Quality Engineering, 36(2), 287-315. https://doi.org/10.1080/08982112.2023.2206479
[57] Kanitz, R., Gonzalez, K., Briker, R., & Straatmann, T. (2023). Augmenting organizational change and strategy activities: Leveraging generative artificial intelligence. The Journal of Applied Behavioral Science, 59(3), 345-363. https://doi.org/10.1177/00218863231168974
[58] Stamler, J., Vaccaro, O., Neaton, J. D., Wentworth, D. (1993). Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the multiple risk factor intervention trial. Diabetes care, 16(2), 434-444. https://doi.org/10.2337/diacare.16.2.434

Details

Primary Language

English

Subjects

Health Services and Systems (Other)

Journal Section

Review Article

Authors

Meleknur Göktaş ^*
0000-0003-4036-3474
Türkiye

Tuğba Bilgehan
0000-0002-3326-776X
Türkiye

Publication Date

May 1, 2025

Submission Date

December 6, 2024

Acceptance Date

March 18, 2025

Published in Issue

Year 2025 Volume: 5 Number: 1

IZ

https://izlik.org/JA93UZ23BG

Cite

RIS / Bibtex

APA

Göktaş, M., & Bilgehan, T. (2025). A New Era in Diabetes Management: Generative Artificial Intelligence. Artificial Intelligence Theory and Applications, 5(1), 63-81. https://izlik.org/JA93UZ23BG

AMA

1.Göktaş M, Bilgehan T. A New Era in Diabetes Management: Generative Artificial Intelligence. AITA. 2025;5(1):63-81. https://izlik.org/JA93UZ23BG

Chicago

Göktaş, Meleknur, and Tuğba Bilgehan. 2025. “A New Era in Diabetes Management: Generative Artificial Intelligence”. Artificial Intelligence Theory and Applications 5 (1): 63-81. https://izlik.org/JA93UZ23BG.

EndNote

Göktaş M, Bilgehan T (May 1, 2025) A New Era in Diabetes Management: Generative Artificial Intelligence. Artificial Intelligence Theory and Applications 5 1 63–81.

IEEE

[1]M. Göktaş and T. Bilgehan, “A New Era in Diabetes Management: Generative Artificial Intelligence”, AITA, vol. 5, no. 1, pp. 63–81, May 2025, [Online]. Available: https://izlik.org/JA93UZ23BG

ISNAD

Göktaş, Meleknur - Bilgehan, Tuğba. “A New Era in Diabetes Management: Generative Artificial Intelligence”. Artificial Intelligence Theory and Applications 5/1 (May 1, 2025): 63-81. https://izlik.org/JA93UZ23BG.

JAMA

1.Göktaş M, Bilgehan T. A New Era in Diabetes Management: Generative Artificial Intelligence. AITA. 2025;5:63–81.

MLA

Göktaş, Meleknur, and Tuğba Bilgehan. “A New Era in Diabetes Management: Generative Artificial Intelligence”. Artificial Intelligence Theory and Applications, vol. 5, no. 1, May 2025, pp. 63-81, https://izlik.org/JA93UZ23BG.

Vancouver

1.Meleknur Göktaş, Tuğba Bilgehan. A New Era in Diabetes Management: Generative Artificial Intelligence. AITA [Internet]. 2025 May 1;5(1):63-81. Available from: https://izlik.org/JA93UZ23BG