Intelligenza Artificiale per la cura del diabete mellito: opportunità e prospettive future

Artificial Intelligence for diabetes care: opportunities and future perspectives

Diabetes mellitus is globally prevalent, with the International Diabetes Federation predicting 783 million cases by 2045. Type 2 diabetes (T2DM) is the seventh leading cause of death and poses a significant economic burden on global healthcare, demanding innovative approaches amid resource constraints. Moreover, the increasing volume of data requires advanced computational tools for analysis and therapeutic decision support. Digital technologies, particularly artificial intelligence (AI), offer promising solutions. AI, capable of complex tasks like reasoning and language recognition, leverages digitalization in diabetes care, handling electronic health records, imaging diagnostics, and laboratory data. Machine learning, an advanced form of AI, outperforms traditional data analysis by automatically identifying patterns and generating predictions without specific programming. Emerging technologies utilizing natural language processing and machine learning accelerate the digital transformation, enhancing cognitive capabilities. AI applications in diabetes span screening, classification, and therapy management.
Predictive models utilizing machine learning algorithms achieve high accuracy in early diabetes risk identification and aids in understanding T2DM heterogeneity, crucial for precision medicine initiatives. Diagnostic AI tools, particularly for retinopathy, neuropathy, and diabetic foot, enhance accessibility and clinical decision-making. AI’s role extends to predicting and preventing complications, such as ulcers and hypoglycemic events. Challenges include data privacy concerns, algorithmic bias, and the need for transparent decision-making processes. Rigorous optimization of AI data inputs is crucial to avoid misleading outputs. While AI offers tremendous benefits, ethical considerations, regulatory frameworks, and human oversight remain critical for responsible and effective implementation in diabetes care.