Artificial intelligence (AI) has gained attention for various reasons in recent years, surrounded by speculation, concerns, and expectations. Despite being developed since 1960, its widespread application took several decades due to limited computing power. Today, engineers continually improve system capabilities, enabling AI to handle more complex tasks. Fields like diagnostics and biology benefit from AI’s expansion, as the data they deal with requires sophisticated analysis beyond human capacity. This review showcases AI’s integration in endocrinology, covering molecular to phenotypic patient data. These examples demonstrate AI’s potential and power in research and medicine.

For the past 100 years, insulin supplementation has been the mainstay of treatment for type 1 diabetes (T1D), which is characterized by progressive autoimmune-mediated loss of insulin-producing β cells in the islets of Langerhans over the last decades, technological advances in glucose monitoring and therapeutics have greatly improved the care and management of these patients. However, morbidity, mortality, and quality of life remain challenges for patients with T1D. Islet transplantation has been successfully performed, but there are several limiting factors, such as the lack of cadaveric donors and the need for lifelong immunosuppressive therapy. Therefore, there is a great medical need for alternative therapeutic approaches. In the current review, the current knowledge on novel approaches for the treatment of T1D with a focus on the potential of using chimeric antigen receptor (CAR)-T cells and natural killer (NK) cells is summarized.