ReviewInternational journal of chronic obstructive pulmonary disease2026
Innovative Applications and Challenges of Artificial Intelligence in the Whole-Course Management of Chronic Obstructive Pulmonary Disease.
Review in International journal of chronic obstructive pulmonary disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence for Early Detection and Prediction of Chronic Obstructive Pulmonary Disease Exacerbations.Healthcare (Basel, Switzerland) · 2026Review
- Safety and Feasibility of an Asynchronous AI-Integrated Tele-Pulmonary Rehabilitation Application Among Patients with COPD.International journal of chronic obstructive pulmonary disease · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.