Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
Allergy. 2023 Oct;78(10):2623-2643. doi: 10.1111/all.15849. Epub 2023 Aug 16.
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
医学领域对人工智能（AI）的兴趣呈指数级增长，这使得新的研究问题和对更大、新型数据的分析成为可能。然而，超越概念验证并提供临床价值的应用仍然很少，特别是在过敏领域。本叙述性评论提供了对 AI 核心概念的基本理解，并批判性地讨论了其局限性和开放性挑战，例如数据可用性和偏差，以及克服这些挑战的潜在方向。我们提供了一个概念框架，用于在该领域构建 AI 应用，并讨论了前沿案例示例。这些过敏领域的 AI 和机器学习应用大多涉及监督学习和无监督聚类，重点是诊断和亚型。我们分享了关于 AI 良好实践的指南，以指导读者有效地、安全地应用它，并展望了该领域的进展和增加临床影响的举措。我们预计，人工智能可以进一步加深我们对疾病机制的认识，并为过敏领域的精准医学做出贡献。
