Artificial Intelligence and Machine Learning for Inborn Errors of Immunity: Current State and Future Promise.

Journal: The journal of allergy and clinical immunology. In practice
Published Date: Aug 8, 2024

Abstract

Artificial intelligence (AI) and machine learning (ML) research within medicine has exponentially increased over the last decade, with studies showcasing the potential of AI/ML algorithms to improve clinical practice and outcomes. Ongoing research and efforts to develop AI-based models have expanded to aid in the identification of inborn errors of immunity (IEI). The use of larger electronic health record data sets, coupled with advances in phenotyping precision and enhancements in ML techniques, has the potential to significantly improve the early recognition of IEI, thereby increasing access to equitable care. In this review, we provide a comprehensive examination of AI/ML for IEI, covering the spectrum from data preprocessing for AI/ML analysis to current applications within immunology, and address the challenges associated with implementing clinical decision support systems to refine the diagnosis and management of IEI.

Authors

  • Alexandra K Martinson
    Department of Pediatrics, Children's National Hospital, Washington, DC.
  • Aaron T Chin
    Department of Pediatrics, University of California, Los Angeles, Los Angeles, CA, United States.
  • Manish J Butte
    Department of Pediatrics and Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, United States.
  • Nicholas L Rider
    Department of Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Division of Allergy-Immunology Carilion Clinic, Roanoke, VA, United States.

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