厦门大学学报（自然科学版）2024，Vol.63Issue(5)：894-905,12.DOI:10.6043/j.issn.0438-0479.202402019
人工智能驱动慢性阻塞性肺疾病精准诊疗研究进展
Research progress on artificial intelligence driving precision diagnosis and treatment of chronic obstructive pulmonary disease
摘要
Abstract
[Background]Chronic obstructive pulmonary disease(COPD)is a complex and prevalent respiratory disorder with irreversible airflow limitation worldwide.Precision diagnosis and treatment at its early stage significantly improve the quality of life of patients.COPD symptoms are diverse and progressive,e.g.,chronic cough,sputum production,dyspnea and chest tightness,indicating advances in COPD.While the pathophysiology of COPD is multifaceted with persistent airway inflammation,airway remodeling,and alveolar destruction,the etiology of COPD is multifactorial,including prolonged smoking,environmental pollutants,occupational hazards,and genetic predispositions.These factors collectively result in airflow obstruction and pathological changes in the respiratory tract.Specifically,the progression of COPD is often accompanied with persistent inflammatory responses,oxidative stress,and intensive pulmonary damage.[Progress]Pulmonary function tests(PFTs)are routinely performed to examine COPD,providing physicians with a ratio of the forced expiratory volume in one second by the forced vital capacity to evaluate COPD.Unfortunately,the results of PFTs are critically affected by the effort of patients,and the interpretation of PFTs also depends on experience and skills of physicians.While PFTs allow physicians to quantify the severity of COPD,they do not reach a specific diagnosis and are commonly associated with medical history,physical examination such as CT imaging,functional MR imaging and respiratory sound,and laboratory data to determine a diagnosis.Therefore,physicians expect more precise COPD diagnosis and treatment methods than conventional ones to improve patient's quality of life.Nowadays artificial intelligence(AI)is widely discussed in precision medicine.Specifically,AI techniques or mathematical models are also increasingly used in COPD diagnosis,treatment,monitoring,and management.These models are generally categorized into unimodal and multimodal AI models in accordance with clinical COPD data.While the unimodal model uses only a single one modality such as PFTs or CT images,the multimodal model fuses a diversity of data including imaging,biomedical information,and clinical records.All these models generally provide physicians with a holistic assessment of COPD,patient-specific treatment for precision medicine.[Perspective]In general,AI techniques provide a promising way to precisely diagnose and treat COPD in its early stage,as well as COPD management and monitoring.Specifically,artificial general intelligence,generative artificial intelligence,multimodal large language models are innovating clinical methods in diagnosis,treatment,monitoring,and management of pulmonary diseases,although they still suffer from medical data privacy and security,model generalizability,interpretability and complexity,legal and ethical issues.Future research should address these issues in various angles.It is essential to strengthen privacy protection and security measures.Moreover,it is vital to improve the generalizability,transparency and interpretability and reduce the complexity of various AI models in clinical applications.Additionally,medical ethics are important when applying AI techniques to precision pulmonary medicine.关键词
慢性阻塞性肺疾病/人工智能/单模态数据/多模态数据/生成式人工智能/通用人工智能/多模态大语言模型/呼吸病学/精准医学Key words
chronic obstructive pulmonary disease/artificial intelligence/unimodal data/multimodal data/artificial general intelligence/generative artificial intelligence/multimodal large language model/respiratory medicine/precision medicine分类
医药卫生引用本文复制引用
朱子锐,曾卓,曾惠清,罗雄彪..人工智能驱动慢性阻塞性肺疾病精准诊疗研究进展[J].厦门大学学报（自然科学版）,2024,63(5):894-905,12.基金项目
国家自然科学基金(82272133) （82272133）
