**3. Results**

Table 2 gives the basic characteristic of the research papers. There has been an increased interest in studies applying AI to diabetes during 1991–2018. The number of papers has been growing gradually, with two-thirds of the total of 372 papers being published in the 2014–2018 period. Notably, the papers being published in 2009 have the highest total citations, mean cite rate and mean use rate in the last five years (2014–2018).


**Table 2.** General characteristics of publications.

<sup>1</sup> Total usage: Total number of download; <sup>2</sup> Use rate: Total number of downloads/Total number of papers.

In terms of study settings (i.e., the country where the study was conducted), there were a total of 36 countries being mentioned in the abstracts of 372 publications (see Table 3). Of those, the United States of America was mentioned 44.1%, followed by Ireland (10.2%) and Italy (6.1%). The top 10 countries accounted for over 80% of the total study settings. Noticeably, in some countries where the prevalence of diabetes is higher than others [14] such as Saudi Arabia (*n* = 3, 1.2%), Egypt (*n* = 1, 0.4%), and United Arab of Emirates (*n* = 2, 0.8%), the number of papers was small compared to others with less prevalence of diabetes like Ireland, Italy, and Japan.


**Table 3.** Number of papers by countries as study settings.

By analyzing the keywords and abstracts' contents, it provided us with a clearer comprehension of the scopes of studies and development of research landscapes. Figure 1 describes the co-occurrence of keywords with the most common groups of terms. There were 17 major clusters that emerged from 165 most common keywords with co-occurrence of 5 times and higher. Of which, we could arrange into three major clusters: (1) AI types and its application in diabetes such as red cluster (machine learning, deep learning and clinical predictions), turquoise cluster (data mining and type II diabetes diagnosis), and green cluster (artificial neural network, big data and gene expression in diabetes diagnosis); (2) diabetes types: light yellow cluster and blue cluster (type II diabetes) and orange cluster (type 1 diabetes); (3) robotics in surgery, risk factor (obesity) and epidemiology of diabetes.

frequentkeywords.principalcomponentsscaled to the keywords' occurrences; the thickness of the lines was drawn based on the strength of the association between two keywords.

As for the content analysis of abstracts, the top 20 emerging research domains that were highlighted via the exploratory factor analysis of abstracts are listed in Table 4**.** Some AI techniques have been most used in this dataset including fuzzy expert system, support vector machine, artificial neural network, and machine learning. Those branches were applied in the following fields of diabetes: (1) clinical prediction (health records collecting or support vector machine modeling for prediction); (2) diabetes management (monitor blood sugar levels); (3) robot-assisted surgery with complication (hypertension, or obesity); (4) the cost of diabetes care.


**Table 4.** Top 20 research domains emerged from exploratory factor analysis of all abstracts' contents.

**<sup>1</sup>** UCI: Machine Learning Repository; **<sup>2</sup>** DM: Diabetes mellitus; **<sup>3</sup>** AUC: Area Under the Curve.

The co-occurrence of the most frequent landscape was shown in Figure 2 by using exploratory factor analysis. In particular, we have the following major landscapes: (1) AI techniques in diabetes diagnosis (machine learning, Support Vector Machine (SVM), Fuzzy) (red); (2) diabetes prediction using model (green); (3) risk factors prediction (yellow and orange); and (4) diabetes treatments.

**Figure 2.** Co-occurrence of most frequent topics emerged from exploratory factor analysis of abstracts contents.

In Table 5, we showcased the research topics that were constructed via the use of latent Dirichlet allocation where we scrutinized the most frequent words and titles for each topic and manually annotated the labels of the topics. The topics found to have the highest volumes of publications included: (1) AI application in diabetes prediction and diagnosis; (2) complications of diabetes prediction; (3) biomedicine and molecular biology in diabetes; (4) e-health for diabetes care, and (5) robot-assisted surgery for patients with diabetes.


**Table 5.** 10 research topics classified by Latent Dirichlet Allocation

In Figure 3, we illustrate the changes in research productivity over time. It shows that the number of publications related to AI in diabetes increased during the research period, especially from the beginning of the 21st century, especially in Topic 1 and Topic 2.

**Figure 3.** Changes in applications of Artificial Intelligence to diabetes research during 1991–2018.

Based on WOS categories, we identified the dendrogram for those (Figure 4). The horizontal axis shows the dissimilarity between research areas. The vertical position of the split, shown by a short bar indicates the dissimilarity between research areas. AI in diabetes focused on the following research areas: (1) computer Application in health care and biomedicine for diabetes; (2) biotechnological investigation and physiological mechanisms of diabetes; (3) AI application in biomedicine and comorbidities of diabetes; (4) health policy and diabetes, and (5) technology and diabetes.

**Figure 4.** The clustering of research disciplines (WOS classification) used in Artificial Intelligence and Diabetes.
