ArticleBarcelona respiratory network reviews
Artificial Intelligence in COPD: New Venues to Study a Complex Disease.
Article in Barcelona respiratory network reviews. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Article
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Multimodal data-driven multitask learning for enhanced identification and classification of chronic obstructive pulmonary disease: a retrospective study.Journal of global health · 2026Article
- A Narrative Review of the Correlation Between Comorbidity and Acute Exacerbation of COPD Patients.International journal of chronic obstructive pulmonary disease · 2026Review
- PulmoClass-3DAtt: A Self-Attention Network for Classification of COPD, PRISm and Normal.International journal of chronic obstructive pulmonary disease · 2026Article
- Advances in artificial intelligence applications for the management of chronic obstructive pulmonary disease.Frontiers in medicine · 2025Review
- Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Management of Refractory Chronic Obstructive Pulmonary Disease: A Review.Life (Basel, Switzerland) · 2024Review
- Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.Journal of cardiovascular development and disease · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories. Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems. AI has shown impressive results in areas related to automated clinical decision making, radiological interpretation and prognostication. The unique nature of COPD and the accessibility to well-phenotyped populations result in an ideal scenario for AI development. This review provides an introduction to AI and deep learning and presents some recent successes in applying AI in COPD. Finally, we will discuss some of the opportunities, challenges, and limitations for AI applications in the context of COPD.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.