ReviewBreathe (Sheffield, England)2024
Artificial intelligence in respiratory care: perspectives on critical opportunities and challenges.
Review in Breathe (Sheffield, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Standardising terminology in routine electronic health records for respiratory endpoints: why we still can't agree.Thorax · 2026Article
- Harnessing Machine Learning and Electronic Health Record Data to Improve Asthma Management.Current allergy and asthma reports · 2026Review
- Pulmonary toxicity of immune checkpoint inhibitors.Breathe (Sheffield, England) · 2026Review
- Artificial Intelligence-Based Automated Analysis for Pleural Effusion Detection on Thoracic Ultrasound: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Decoding the "black-box": explainable artificial intelligence towards trustworthy advancement in respiratory medicine.Breathe (Sheffield, England) · 2026Article
- Innovation in technologies for monitoring lung function in patients with respiratory disease.Breathe (Sheffield, England) · 2025Article
- Advances in Therapeutics for Chronic Lung Diseases: From Standard Therapies to Emerging Breakthroughs.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is transforming respiratory healthcare through a wide range of deep learning and generative tools, and is increasingly integrated into both patients' lives and routine respiratory care. The implications of AI in respiratory care are vast and multifaceted, presenting both promises and uncertainties from the perspectives of clinicians, patients and society. Clinicians contemplate whether AI will streamline or complicate their daily tasks, while patients weigh the potential benefits of personalised self-management support against risks such as data privacy concerns and misinformation. The impact of AI on the clinician-patient relationship remains a pivotal consideration, with the potential to either enhance collaborative care or create depersonalised interactions. Societally, there is an imperative to leverage AI in respiratory care to bridge healthcare disparities, while safeguarding against the widening of inequalities. Strategic efforts to promote transparency and prioritise inclusivity and ease of understanding in algorithm co-design will be crucial in shaping future AI to maximise benefits and minimise risks for all stakeholders.
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.