*4.6. Challenges in the Use of AI*

The increasingly deeper integration of AI applications in the diagnosis, management, and prediction of diabetes has also come with challenges. Ethical issues, in particular the privacy and confidentiality of patient data, have been a topic of debate in existing literature [26]. It is noted that most of the AI technology is still under development and not yet utilized in clinical practice. For this to happen, AI needs to be more developed so that it can be as sensitive and specific as possible. This requires large datasets to train the AI or computer models. These datasets may consist of sensitive and confidential patient biodata such as their age, body mass index, etc. The use of wearable technology that constantly records data on behavior of diabetic users while also encouraging users to input their sensitive health data for it to provide more accurate, tailor-made suggestions would inherently expose users to the risk of having personal data leaked or being used for other purposes without their consent. Furthermore, with large datasets, massive amounts of data may lead to difficulty in human efforts to design intricate and perfectly logical models for specific clinical tasks or to provide

appropriate, effective treatment, or behavior suggestions for users. Other common limitations of mathematical models such as the lack of model validations, especially for model of novel approach, data/measurement bias, issues in sample representations, and the occurrence of outliers, would also potentially be found with AI models. Therefore, further research and development would be needed to tackle these problems before AI may be reliably used in clinical practice as well as in diabetic care.

This bibliometric analysis has put forward several key developments in the field of AI in diabetes and has included large quantities of literature on the topic of interest. However, since the study has only included papers written in the English language, a bias towards Western countries may be present. In addition, another limitation of the study was that of a restriction to purely peer-reviewed research publications, which possibly could have affected the extensiveness of the analyzed results. Future studies may also benefit from applying sensitivity analysis for type I and type II diabetes when conducting research using the method applied in this study.
