Zhonghua Jie He He Hu Xi Za Zhi. 2026 Jan 12;49(1):78-83. doi: 10.3760/cma.j.cn112147-20250728-00445.
ABSTRACT
Rare lung diseases are a group of diseases characterized by significant clinical heterogeneity, challenging diagnosis and treatment processes, and diverse underlying causes. Due to their uncommon symptoms and limited awareness among healthcare providers, these diseases are frequently misdiagnosed or diagnosed too late, resulting in poor patient outcomes and placing a significant healthcare burden on the healthcare system. However, in recent years, the rapid advancements in artificial intelligence (AI) technology within the medical field have created new opportunities for early identification, accurate diagnosis, and personalized management of these diseases. A variety of AI techniques, ranging from traditional machine learning to more recent methods such as deep learning, reinforcement learning, and transfer learning, have been employed in areas such as clinical decision support, radiomics, omics data analysis, and the prediction of treatment responses for rare lung diseases. This article systematically reviews the latest research progress of AI applications in idiopathic pulmonary fibrosis, cystic fibrosis, idiopathic pulmonary arterial hypertension, and other rare lung diseases. It also emphasizes AI's potential benefits in disease classification, treatment evaluation, and prognosis prediction through illustrative research examples.
PMID:41483922 | DOI:10.3760/cma.j.cn112147-20250728-00445