Department of Pediatrics, Children's National Hospital, Washington, DC.
Department of Pediatrics, Division of Immunology, Allergy and Rheumatology, University of California, Los Angeles, Los Angeles, Calif.
J Allergy Clin Immunol Pract. 2024 Oct;12(10):2695-2704. doi: 10.1016/j.jaip.2024.08.012. Epub 2024 Aug 8.
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.
人工智能（AI）和机器学习（ML）在医学领域的研究在过去十年中呈指数级增长，研究展示了 AI/ML 算法改善临床实践和结果的潜力。正在进行的研究和开发基于 AI 的模型的努力已经扩展到帮助识别先天性免疫缺陷（IEI）。使用更大的电子健康记录数据集，加上表型精度的提高和 ML 技术的增强，有可能大大提高 IEI 的早期识别，从而增加获得公平护理的机会。在这篇综述中，我们全面探讨了 AI/ML 在 IEI 中的应用，涵盖了从 AI/ML 分析的数据预处理到免疫学中的当前应用，并讨论了与实施临床决策支持系统相关的挑战，以完善 IEI 的诊断和管理。
