The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use

Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review

Artificial intelligence (AI) has been applied in healthcare for diagnosis, treatments, disease management, and for studying underlying mechanisms and disease complications in diseases like diabetes and metabolic disorders. This review is a comprehensive overview of various applications of AI in the healthcare system for managing diabetes. A literature search was conducted on PubMed to locate studies integrating AI in the diagnosis, treatment, management and prevention of diabetes. As diabetes is now considered a pandemic now so employing AI and machine learning approaches can be applied to limit diabetes in areas with higher prevalence. Machine learning algorithms can visualize big datasets, and make predictions. AI-powered mobile apps and the closed-loop system automated glucose monitoring and insulin delivery can lower the burden on insulin. AI can help identify disease markers and potential risk factors as well. While promising, AI's integration in the medical field is still challenging due to privacy, data security, bias, and transparency. Overall, AI's potential can be harnessed for better patient outcomes through personalized treatment.

Trends and Barriers in Diabetic Retinopathy Screening: Korea National Health and Nutritional Examination Survey 2016-2021

BACKGROUND

The prevalence of diabetes is increasing globally, highlighting the importance of preventive healthcare. This study aimed to identify the diabetic retinopathy (DR) screening rates and risk factors linked to DR screening nonadherence in the Korean population through a nationally representative sample survey.

METHODS

Among the Korea National Health and Nutrition Examination Survey database from 2016 to 2021, participants aged ≥ 40 years with diabetes were included. The weighted estimate for nonadherence to DR screening within a year was calculated. Risk factor analyses were conducted using univariate and multivariate logistic regression.

RESULTS

Among the 3,717 participants, 1,109 (29.5%) underwent DR screening within the past year, and this national estimate exhibited no statistically significant difference from 2016 to 2021 (P = 0.809). Nonadherence to annual DR screening was associated with residing in rural areas, age ≥ 80 years, low educational level, self-reported good health, absence of ocular disease, current smoking, lack of exercise and dietary diabetes treatment, and no activity limitation (all P < 0.05).

CONCLUSION

The recent DR screening rate in Korea was relatively low. Factors associated with apathy and complacency towards personal health were associated with the nonadherence to DR screening. Educational interventions have the potential to enhance the annual screening rate for diabetic patients.