ReviewFrontiers in medicine2025
Advances in artificial intelligence applications for the management of chronic obstructive pulmonary disease.
Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Anti-type 2 monoclonal antibody therapy for eosinophilic COPD beyond triple therapy: a systematic review and meta-analysis with strict eosinophil inclusion criteria.Frontiers in medicine · 2026Pooled it
- Uncovering sodium overload-associated gene signatures in chronic obstructive pulmonary disease through integrated bioinformatics and machine learning.Journal of thoracic disease · 2026Article
- Review
- Interpretable machine learning for predicting in-hospital mortality in COPD ICU patients: a rigorous validation across time and geography.Respiratory research · 2026Article
- Review
- The Evolving Landscape of COPD Typization.Medicina (Kaunas, Lithuania) · 2026Review
- Facilitators and Barriers of Using an Artificial Intelligence Agent in Chronic Disease Management: A Normalization Process Theory-Guided Qualitative Study of Older Patients with COPD.Healthcare (Basel, Switzerland) · 2026Article
- Multimodal data driven machine learning approach for enhancing diagnostic accuracy and efficiency in pulmonary disease management within primary healthcare settings.Frontiers in medicine · 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
Chronic obstructive pulmonary disease (COPD), characterized by high incidence and mortality rates, is a chronic respiratory disorder that places a substantial burden on healthcare systems. Artificial Intelligence (AI), with its deep integration into the medical field, particularly through its core branches-Machine Learning (ML) and Deep Learning (DL)-has demonstrated significant potential in the intervention and management of COPD. From early risk prediction based on multimodal data to the enhancement of precise diagnosis and treatment through radiomics and clinical decision support systems, and further to the dynamic assessment of acute exacerbation and comorbidity risks via machine learning models, AI has, in combination with bioinformatics and multi-omics analysis, established a novel intelligent management framework that spans the entire disease continuum. This framework offers innovative, individualized solutions aimed at alleviating the burden on healthcare systems. This article reviews the technical applications and clinical value of AI in the diagnosis, prevention, treatment, and prognosis of COPD, discusses current challenges, and outlines future development directions to provide insights for clinical practice and research.
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.