Innovative Applications and Challenges of Artificial Intelligence in the Whole-Course Management of Chronic Obstructive Pulmonary Disease

Shanji Chen,1– 3,* Shisi Xing,1,3,* Guanghui Zhang,1,4 Fei Qiu1,3 1Department of Engineering Research Center, The First Affiliated Hospital of Hunan University of Medicine, Huaihua, Hunan, People’s Republic of China; 2Faculty of Public Health, Mahasarakham University, Kham Riang, Mahasarakham, Thailand; 3Department of Medical Technology, Hunan Primary Digital Engineering Technology Research Center for Medical Prevention and Treatment, Huaihua, Hunan, People’s Republic of China; 4The First Affiliated Hospital, Hunan University of Medicine, Huaihua, Hunan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shanji Chen, Department of Engineering Research Center, The First Affiliated Hospital of Hunan University of Medicine, Huaihua, Hunan, 418000, People’s Republic of China, Tel +86 15274921139, Email [email protected] Fei Qiu, Department of Engineering Research Center, The First Affiliated Hospital of Hunan University of Medicine, Huaihua, Hunan, 418000, People’s Republic of China, Tel +86 13607411442, Email [email protected]: To systematically map how artificial intelligence (AI) can transform whole-course chronic obstructive pulmonary disease (COPD) management across prevention, diagnosis, treatment and rehabilitation within a 4P (Predictive, Preventive, Personalized, Participatory) medicine framework, and to identify actionable strategies for overcoming current barriers.Methods: A systematic search of PubMed, Web of Science and Embase was performed for articles published between January 2021 and June 2025. This review was conducted following the PRISMA guidelines. Forty empirical studies and reviews that applied AI/ML to COPD prevention, early detection, personalised therapy, exacerbation prediction or pulmonary rehabilitation were critically appraised. Data were extracted on technical foundations, data modalities, algorithms, validation metrics and implementation outcomes.Results: AI models integrating multimodal data (imaging, wearables, environmental exposures, genomics) achieved AUC ≥ 0.80 for predicting acute exacerbations up to seven days in advance, were associated with a reduction in emergency visits of up to 98% and a lowering of readmission rates by 25– 48%. Screening tools using chest X-ray, CT or smartphone sensors attained ≥ 90% accuracy for early COPD detection in primary-care settings. Personalised treatment optimisation was linked to a 53% lowering of exacerbation risk in best-responding subgroups. Home-based AI rehabilitation platforms increased adherence by > 30% without additional equipment. Key implementation challenges include data heterogeneity, limited explainability, digital divide among older adults and unclear regulatory frameworks.Conclusion: AI is poised to operationalise 4P COPD care, delivering substantial clinical and economic benefits. Future success depends on cross-centre data standards, explainable-AI toolchains, federated learning and inclusive reimbursement policies.Keywords: chronic obstructive pulmonary disease, artificial intelligence, machine learning, exacerbation prediction, precision medicine, pulmonary rehabilitation