**5. Conclusions**

The application of AI in diabetes has evidently become more common in recent years, with AI-related technologies being found to assist from diagnosis and clinical treatment to daily management of diabetes. As a condition with severity depending heavily on the lifestyle and behavior of patients and those at risk, continuous monitoring and initiating specific treatments based on data of individual's conditions and behavior as well as their specific surrounding would likely be effective, and is an area that AI applications can be seen as a promising solution. With such opportunities to enhance diabetes management also come challenges associated with the use of AI, of which privacy and confidentiality remain the major ones. These issues must be tackled and resolved before clinical use of AI-related technology is approved and available for the patient's benefit.

**Author Contributions:** Conceptualization, G.T.V., B.X.T, R.S.M., K.K.G and R.C.M.H; Data curation, G.T.V., R.S.M, H.Q.P, H.T.P. and G.H.H.; Formal analysis, G.T.V., B.X.T., H.Q.P. and H.T.P.; Investigation, G.T.V., K.K.G. and R.C.M.H.; Methodology, G.T.V., B.X.T. and H.T.P.; Software, R.S.M., H.Q.P. and G.H.H.; Supervision, B.X.T., C.A.L. and C.S.H.H.; Validation, C.A.L., R.C.M.H. and C.S.H.H; Visualization, G.H.H.; Writing – original draft, G.T.V. and K.K.G.; Writing – review & editing, C.A.L., R.C.M.H. and C.S.H.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflicts of interest.
