Fuse Yutaro, Murphy Shawn N, Ikari Hisahiro, Takahashi Akiko, Fuse Kenshiro, Kawakami Eiryo
Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
Massachusetts General Hospital, Boston, MA, USA.
Allergol Int. 2025 Oct;74(4):499-513. doi: 10.1016/j.alit.2025.06.005. Epub 2025 Aug 19.
Recent advances in computing technology and the development of data utilization environments have rapidly accelerated the application of artificial intelligence in clinical research and healthcare. This review provides a comprehensive overview of current machine learning techniques for analyzing clinical data, with illustrative examples from the field of allergic diseases. In addition to conventional methods for clinical data analysis, we discuss emerging approaches including medical image analysis and time-series modeling of electronic health record data. Recent developments such as large language models and foundation models trained on massive datasets are also discussed. Looking ahead, we explore future directions in analytical methodology, including mathematical modeling, interpretable artificial intelligence, and multimodal learning that integrates various data types. We also introduce the concept of the digital twin-a virtual representation of an individual patient that simulates disease progression and treatment response-as a promising concept for advancing precision medicine. Finally, we discuss the essential role of physicians in the development and implementation of machine learning tools and discuss emerging ethical issues such as fairness, privacy, and patient autonomy. By synthesizing recent technical advances with clinical relevance, this review aims to provide clinicians and researchers with a practical and forward-looking guide to machine learning in clinical medicine, including its growing application in the field of allergy.
计算技术的最新进展以及数据利用环境的发展迅速加速了人工智能在临床研究和医疗保健中的应用。本综述全面概述了当前用于分析临床数据的机器学习技术，并列举了过敏疾病领域的实例。除了临床数据分析的传统方法外，我们还讨论了新兴方法，包括医学图像分析和电子健康记录数据的时间序列建模。还讨论了诸如在大规模数据集上训练的大语言模型和基础模型等最新进展。展望未来，我们探索分析方法的未来方向，包括数学建模、可解释人工智能以及整合各种数据类型的多模态学习。我们还介绍了数字孪生的概念——模拟疾病进展和治疗反应的个体患者的虚拟表示——作为推进精准医学的一个有前景的概念。最后，我们讨论了医生在机器学习工具开发和实施中的重要作用，并讨论了公平性、隐私和患者自主权等新出现的伦理问题。通过将近期技术进展与临床相关性相结合，本综述旨在为临床医生和研究人员提供一份关于临床医学中机器学习的实用且具有前瞻性的指南，包括其在过敏领域日益增长的应用。
