Recent strides in diabetes research have increasingly leveraged machine learning and deep learning techniques, marking a pivotal shift in understanding and managing this complex disease. A comprehensive bibliometric analysis from 2000 to 2022 has shed light on the evolving landscape of diabetes-related scientific literature, uncovering global trends and key insights crucial for future advancements.

Using the Scopus database, researchers meticulously examined numerous scholarly articles to discern prominent themes and contributions in the field. The analysis categorized findings into three main domains: detection, prediction, and management. Under detection, encompassing diagnosis, screening, and segmentation, innovative applications of AI have enabled more accurate and efficient early detection methods.

In the realm of prediction, including prognosis and forecasting, machine learning models have shown remarkable capability in anticipating disease progression and outcomes, offering invaluable insights for personalized patient care strategies. Moreover, in the management domain, which spans treatment, control, monitoring, and patient education, AI-driven approaches have facilitated enhanced treatment protocols and integrated telemedicine solutions.

Key findings highlighted influential countries, institutions, and journals driving advancements in diabetes research using AI. This robust analysis, facilitated by tools like Biblioshiny and RStudio, underscores the growing significance of AI in transforming healthcare practices related to diabetes.

As this field continues to evolve, the insights gleaned from this bibliometric study promise to guide future research endeavors, paving the way for more precise diagnostics, personalized therapies, and improved outcomes for individuals with diabetes worldwide. Stay informed with our updates as AI redefines the boundaries of diabetes research and care.