AI has the potential to revolutionize diabetes research at multiple stages of diabetes management by enabling earlier detection, personalized treatment plans, and improved monitoring through predictive analytics. With machine learning algorithms, AI can analyze large datasets to identify patterns in glucose levels, lifestyle factors, and genetic data, leading to more effective and individualized diabetes management.
This article explores the potential of artificial intelligence and machine learning in improving diabetes care, including aspects related to public health issues. It discusses how these technologies can be utilized to address public health issues in diabetes management and outcomes.
Current guidelines recommend annual eye exams for individuals with diabetes to detect diabetic retinopathy (DR), a leading cause of blindness, and AI algorithms have been developed to autonomously screen for DR using fundus photography. This review discusses the development and validation of AI algorithms for DR detection, highlights variability in reference standards, and examines issues like cost-effectiveness, and bias in implementing AI in clinical practice.
This review provides insights into the current state of and limitations of AI models for Type 2 Diabetes Mellitus prediction and highlights the challenges associated with their development and clinical implementation
Public health issues in diabetes stem from Public Health in access to care, education, and resources among different socioeconomic and racial groups. Marginalized communities often face higher rates of diabetes due to limited access to healthy foods, healthcare services, and preventative education, leading to worse health outcomes. These inequities are compounded by social determinants of health, including income, housing, and access to insurance, which contribute to the disproportionate burden of diabetes in these populations.
AI has the potential to advance Public health issues in diabetes research by identifying Public Health in care, outcomes, and access across diverse populations. By analyzing data from underrepresented groups, AI can help tailor interventions to address social determinants of health, improving access to personalized diabetes care. This can lead to more outcomes and reduce the burden of diabetes in marginalized communities. However, the use of AI in diabetes research poses risks to public health issues, as biases in training data could lead to unequal treatment recommendations for underrepresented groups. If AI systems are not designed with diverse populations in mind, they may reinforce existing Public Health in diabetes care and outcomes. Additionally, unequal access to AI-driven technologies could further widen the gap in healthcare for marginalized communities.