Diabetes Mellitus is comprised of two categories: Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM). It is one of the most frequently occurring non-communicable illnesses in the world today, with a global prevalence rate of 537 million people living with diabetes worldwide among the adult population. Traditional techniques used to treat diabetes mellitus cases have been rendered insufficient as far as ensuring personalized care that provides timely regulation of the blood sugar levels is concerned. The advancements in the field of artificial intelligence technologies have brought about revolutionary methods of managing diabetes, including diagnostics, treatment, and support. This systematic review followed the PRISMA guidelines for research, including articles obtained from PubMed, Web of Science, and Scopus databases up to April 2026. There were twenty-two articles that fulfilled the criteria for selection and quality. The application of artificial intelligence technology led to statistically significant improvements in HbA1c, TIR, early identification of micro- and macrovascular complications, personalized insulin therapy, and increased engagement and empowerment of patients. Highly sophisticated algorithms such as recurrent neural networks, transformers, gradient-boosted machine ensembles, and reinforcement learning agents were shown to be very effective in predicting hypoglycemia and risk stratification. The digital health interventions helped increase the compliance rate of patients with treatment regimens by 25-35%. Nevertheless, some issues were still present such as inequities in access to advanced technologies, a lack of multicentric and multi-ethnic validation, regulatory uncertainties, and immaturity with respect to data governance. Artificial intelligence is revolutionizing diabetes care with an emphasis on prediction, prevention, personalization, and participation.

Introduction: Variant RARA rearrangements in acute promyelocytic leukemia (APL) are rare and may present with atypical morphologic and immunophenotypic features, leading to diagnostic challenges. We report a case of a 9-year-old female child presenting with clinical and morphologic features suggestive of acute myeloid leukemia (AML) with monocytic differentiation. Flow cytometry supported a diagnosis of acute myelomonocytic leukemia. However, conventional cytogenetic analysis revealed a variant t(11;17)(q23;q21) translocation involving the RARA gene. This case underscores the importance of integrated morphologic, immunophenotypic, and cytogenetic evaluation for accurate classification and therapeutic stratification.