ARTIFICIAL INTELLIGENCE IN LABORATORY DIAGNOSTICS OF IRON DEFICIENCY ANEMIA (review of literature)

The review is devoted to the potential use of artificial intelligence in the diagnosis of iron deficiency anemia (IDA) using machine learning (ML) based on clinical blood count (CBС) data and laboratory indicators of iron metabolism. The paper presents studies on the development, implementation and evaluation of ML algorithms that: 1) allow predicting the concentration of iron metabolism indicators (in particular, serum ferritin) based on a minimum set of laboratory tests (regression algorithms; 2) automatically assess the risk of iron deficiency in the body, reflected by a low level of ferritin in the blood serum of patients with anemia (classification algorithms); 3) perform second line diagnostic tests on existing specimens based on the results of initially ordered tests (reflex testing algorithms). © 2025 Elsevier B.V., All rights reserved.