Machine learning application in autoimmune diseases: State of art and future prospectives - Danieli, Maria Giovanna; Brunetto, Silvia; Gammeri, Luca; Palmeri, Davide; Claudi, Ilaria; Shoenfeld, Yehuda; Gangemi, Sebastiano (Autoimmunity reviews, 2024)
Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach. •Machine learning allows us to analyze large amounts of data with minimal human intervention.•ML allows the creation of diagnostic algorithms useful for early identification of systemic and organ-specific autoimmune diseases.•It is possible to create predictive algorithms for organ damage to minimize the complications of patients with systemic autoimmune diseases.•ML allows the clinician to identify specific subgroups of patients and guide them towards the correct therapeutic approach.