Modelagem de um algoritmo de inteligência artificial para a atenção ao cuidado do diabetes no Brasil

Abstract

According to the 10th edition of the Diabetes Atlas of the International Diabetes Federation, about 537 million adults live with Diabetes Mellitus (DM) in the world, of which 15.7 million are from Brazil. Some studies aim to address Artificial Intelligence (AI) to help patients self-manage DM and facilitate interactions between patients and health professionals. Self-management is the key to DM treatment. Objective: To create a broad AI algorithm focused on the rehabilitation of these patients, aiming at reducing the complications of the disease. Methods: Free access software was screened and analyzed in relation to criteria of accessibility, usability, adequacy of design standards and User Experience (UX), in addition to the detailed focus on DM self-management. Inclusion criteria: self-management of the disease, cost for the Unified Health System (SUS) and data security according to the data protection law. Results and Discussion: 125 software with AI were analyzed, of which 72% showed the presence of functions and modalities consistent with the proposed proposal and that met international accessibility criteria, being identified as potential allies to minimize costs for the Unified Health System (SUS) in DM care, without compromising the quality of the service offered to the population. In particular, after this analysis, an algorithm was constructed with recommendations for large-scale applicability, forwarded to the Brazilian Ministry of Health, together with an educational material that could be distributed throughout the national territory. Conclusion: Once DM is not adequately treated, there is a possibility that the disease may progress to a more severe onset, resulting in complications such as arterial problems and amputations, retinopathies, kidney diseases, diabetic foot. AI emerges as a strong ally to avoid these problems, promoting technological application in the health area and reducing costs to the country, in addition to streamlining the SUS queue. Furthermore, promoting better functionality and quality of life for patients.