ReviewJournal of clinical medicine2024
Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Review
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- Defining physician-AI collaboration in pulmonary and critical care medicine: concepts and illustrative examples for clinical reasoning.ATS scholar · 2026Article
- Improving self-care behaviors in patients with chronic obstructive pulmonary disease using a knowledge-based intelligent self-care system: protocol for a randomized controlled trial.Journal of thoracic disease · 2026Article
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- Prediction of Chronic Obstructive Pulmonary Disease Using Machine Learning, Clinical Summary Notes, and Vital Signs: A Single-Center Retrospective Cohort Study in the United States.Advances in respiratory medicine · 2026Article
- Artificial Intelligence for the Identification of Vascular Imaging Biomarkers in COPD: Redefining Phenotypes and Enabling Precision Care.Journal of clinical medicine · 2025Article
- To take a different approach: Can large language models provide knowledge related to respiratory aspiration?Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic respiratory diseases (CRDs), including asthma and chronic obstructive pulmonary disease (COPD), represent significant global health challenges, contributing to substantial morbidity and mortality. As the prevalence of CRDs continues to rise, particularly in low-income countries, there is a pressing need for more efficient and personalized approaches to diagnosis and treatment. This article explores the impact of emerging technologies, particularly artificial intelligence (AI), on the management of CRDs. AI applications, including machine learning (ML), deep learning (DL), and large language models (LLMs), are transforming the landscape of CRD care, enabling earlier diagnosis, personalized treatment, and enhanced remote patient monitoring. The integration of AI with telehealth and wearable technologies further supports proactive interventions and improved patient outcomes. However, challenges remain, including issues related to data quality, algorithmic bias, and ethical concerns such as patient privacy and AI transparency. This paper evaluates the effectiveness, accessibility, and ethical implications of AI-driven tools in CRD management, offering insights into their potential to shape the future of respiratory healthcare. The integration of AI and advanced technologies in managing CRDs like COPD and asthma holds substantial potential for enhancing early diagnosis, personalized treatment, and remote monitoring, though challenges remain regarding data quality, ethical considerations, and regulatory oversight.
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.