Is it time for the A/I (allergist/immunologist) to embrace AI (artificial intelligence) in diagnosis and treatment of the inborn errors of immunity?

In 1970, W.B. Schwartz predicted that computers would revolutionize medicine by enhancing the physician's intellect, a vision that has largely materialized in the past 5 decades. Recent advancements in artificial intelligence (AI), especially in health care, have transformed AI from a conceptual tool into a fundamental part of clinical practice. AI has been successfully applied in diagnostic imaging, health system management, and patient care workflows. Within immunology, AI's potential for diagnosing and managing complex conditions such as inborn errors of immunity (IEI) is increasingly recognized. This article explores the evolving role of AI in the diagnosis and treatment of IEIs, highlighting its potential to advance precision medicine in allergy/immunology. To illustrate this potential, six representative IEIs were selected, each accompanied by a clinical vignette that summarizes the patient history and laboratory findings. These include severe combined immunodeficiency, common variable immunodeficiency, chronic granulomatous disease, X-linked agammaglobulinemia, Wiskott-Aldrich syndrome, and the activated PI3K delta syndrome. An extensive literature review was conducted in medical literature data bases by applying terms such as primary immune deficiency, inborn errors of immunity (IEIs), and allergy. The search focused on identifying studies that explored the intersection of AI technologies with immunology, particularly with regard to the diagnosis and management of IEIs. The literature review identified a growing body of work on the application of AI in allergy and immunology, with 1907 articles on AI and allergy, 16 of which focused specifically on IEI. AI has shown promise in diagnostic accuracy, particularly in rare and complex immunologic conditions, and in improving the efficiency of clinical decision-making. AI holds significant potential for the allergist/immunologist by revolutionizing the diagnosis and treatment of IEIs. By enhancing diagnostic precision, improving patient care workflows, and enabling personalized treatment strategies, AI can advance the practice of immunology. However, challenges such as data quality, model generalizability, and ethical considerations must be addressed to fully harness AI's capabilities in the clinical setting. This article highlights the transformative potential of AI in immunology and proposes its integration into clinical practice for better patient outcomes.