**1. Introduction**

Diabetes is a chronic medical disease that is characterized by increased levels of blood glucose, which causes microvascular and macrovascular complications with time. This chronic condition is concerning because the prevalence of diabetes has been steadily increasing for the past three decades. In 2014, the worldwide prevalence of diabetes was 8.5% among those aged 18 years and older, a escalation from 4.7% in 1980 [1]. In 2016, an estimated 1.6 million deaths were directly attributable to diabetes alone with World Health Organization (WHO) estimating that diabetes was the seventh leading cause of death in 2016. This figure is expected to rise even further in the future [1].

The global health expenditure on diabetes among people of ages 20 and 79 is expected to rise from USD 376 billion in 2010 to an estimated USD 490 billion in 2030 [2]. All these alarming statistics justify the need for ongoing research and active management to prevent and treat diabetes and its complications to optimize quality of life as well as to reduce the economic healthcare burden.

One of the promising research areas that are ongoing in the field of diabetes, is the use of artificial intelligence (AI). Artificial intelligence is a domain in computer science which accentuates the creation and use of intelligent machines that are able to function and respond like humans. According to a recent 2019 bibliometric study, there has been a tripled fold increase in the number of studies on the applications of AI in the past three years alone, with Diabetes being among the top ten areas of interest [3].

The techniques applied in the research of AI and diabetes include machine learning, artificial neural networks, and natural language processing. These techniques have allowed several applications of AI into the diagnosis and management of diabetes such as the diagnosis of microalbuminuria in type II diabetes patients without the need to measure urinary albumin levels. Microalbuminuria (MA) is a known complication of diabetes and is one of the measures of the renal function in diabetic patients The gold standard of such a diagnosis is to collect 24-hour urine albumin excretion, but with artificial intelligence on the rise, the detection of MA can be done with clinical parameters usually monitored in type II diabetes patients such as age, duration of diabetes, body mass index, and HbA1c (which is the average of blood glucose over the past three months, commonly used to assess diabetic control) [4].

Another promising application of artificial intelligence in diabetes is the development of a model that helps to generate a risk score with the ability to predict future glycemic control in individuals with type II diabetes. Machine learning models have been established and used to forecast the trajectories using clinical parameters such as body mass index, glycated hemoglobin, and triglycerides. This developed model can be used to estimate the patient's journey with diabetes and determine the follow-up period based on their risk score. Patients with higher risk scores should be followed up more closely compared to those who have lower risk scores to optimize their health outcomes, particularly in diabetes [5].

The evidently increasing interest of academics and practitioners in applications of AI in diabetes management sparked a need for having a comprehensive and up-to-date picture of what has been done in terms of research on this topic, highlighting areas that have been investigated, the emerging research domains, and potential research gaps that need further investigation. Informed readers may, in turn, be able to decide on a better direction for their future researches on the topic. To the best of our knowledge, there has not been a publication looking into AI application in diabetes on a comprehensive, global scale. This study is conducted to fill such gap in literature by adopting a combination of bibliometric approach and more complex analysis of title and abstracts of publications.
