Artificial intelligence in clinical data analysis: A review of large language models, foundation models, digital twins, and allergy applications

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.