Abstract
Drug-induced and idiosyncratic cytopenias, including anemia, neutropenia, and thrombocytopenia, present significant challenges in fields like immunohematology and internal medicine. These conditions are often unpredictable, multifactorial, and can arise from a complex interplay of drug reactions, immune abnormalities, and other poorly understood mechanisms. In many cases, the precise triggers and underlying factors remain unclear, making diagnosis and management difficult. However, advancements in artificial intelligence (AI) are offering new opportunities to address these challenges. With its ability to process vast amounts of clinical, genomic, and pharmacovigilance data, AI can identify patterns and risk factors that may be missed by traditional methods. Machine learning algorithms can refine predictive models, enabling earlier detection and more accurate risk assessments. Additionally, AI's role in enhancing patient engagement-through tailored monitoring and personalized treatment strategies-ensures more effective follow-up and improved clinical outcomes for patients at risk of these potentially life-threatening conditions. Through these innovations, AI is paving the way for a more proactive and personalized approach to managing drug-induced cytopenias.