ABSTRACT
BACKGROUND: Artificial intelligence (AI) has many applications in various aspects of our daily life, including health, criminal, education, civil, business, and liability law. One aspect of AI that has gained significant attention is natural language processing (NLP), which refers to the ability of computers to understand and generate human language. OBJECTIVE: This study aims to examine the potential for, and concerns of, using AI in scientific research. For this purpose, high-impact research articles were generated by analyzing the quality of reports generated by ChatGPT and assessing the application's impact on the research framework, data analysis, and the literature review. The study also explored concerns around ownership and the integrity of research when using AI-generated text. METHODS: A total of 4 articles were generated using ChatGPT, and thereafter evaluated by 23 reviewers. The researchers developed an evaluation form to assess the quality of the articles generated. Additionally, 50 abstracts were generated using ChatGPT and their quality was evaluated. The data were subjected to ANOVA and thematic analysis to analyze the qualitative data provided by the reviewers. RESULTS: When using detailed prompts and providing the context of the study, ChatGPT would generate high-quality research that could be published in high-impact journals. However, ChatGPT had a minor impact on developing the research framework and data analysis. The primary area needing improvement was the development of the literature review. Moreover, reviewers expressed concerns around ownership and the integrity of the research when using AI-generated text. Nonetheless, ChatGPT has a strong potential to increase human productivity in research and can be used in academic writing. CONCLUSIONS: AI-generated text has the potential to improve the quality of high-impact research articles. The findings of this study suggest that decision makers and researchers should focus more on the methodology part of the research, which includes research design, developing research tools, and analyzing data in depth, to draw strong theoretical and practical implications, thereby establishing a revolution in scientific research in the era of AI. The practical implications of this study can be used in different fields such as medical education to deliver materials to develop the basic competencies for both medicine students and faculty members.
ABSTRACT
Glycemic control in children with type 1 diabetes mellitus (T1DM) is affected by many factors that may be influenced by their lives and community. To identify the factors associated with glycemic control among children with T1DM in Aseer Region, southwestern Saudi Arabia, a cross-sectional interview study was conducted between 1 July and 30 September 2021, with a representative sample of Saudi children aged between 6 months and 15 years with T1DM or their caregivers visiting the diabetes center at Aseer Region. Newly diagnosed cases (<12 months) were excluded from the study. The study included 171 T1DM pediatric patients aged between 18 months and 15 years. The glycated hemoglobin (HbA1c) level ranged between 6.10% and 15.10% (mean HbA1c = 10.39% ± 1.86%). High HbA1c levels (≥7.5%) were observed in most patients (94.7%). Only two significant factors were found: (1) use of carbohydrate counting; 81.8% of children using carbohydrate counts had high HbA1c levels, compared to 96.6% of children not using carbohydrate counts (p = 0.017), and (2) duration of the disease; 91.5% of children with disease duration of ≤3 years had high levels of HbA1c, compared to 98.7% of children with disease duration exceeding 3 years (p = 0.035). Most children with T1DM in Aseer Region had poor glycemic control. Only two factors were associated with better glycemic control: shorter disease duration and use of carbohydrate counting. Therefore, advising diabetic patients to be on a carbohydrate counting program might improve DM control.
