---
title: "Artificial intelligence applications for dyspnoea management in advanced lung cancer: scoping revi"
id: "bmj-open-1-applications-of-artificial-intelligence-in-dyspnoea-management-for-patients"
canonical_url: "https://medichelpline.com/clinical-feed/bmj-open-1-applications-of-artificial-intelligence-in-dyspnoea-management-for-patients"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "BMJ Open"
source_url: "http://bmjopen.bmj.com/cgi/content/short/16/9/e110915?rss=1"
published_at: "2026-09-22T17:11:09.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Artificial intelligence applications for dyspnoea management in advanced lung cancer: scoping revi
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/bmj-open-1-applications-of-artificial-intelligence-in-dyspnoea-management-for-patients
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** BMJ Open
- **Source URL:** [Original Journal Publication](http://bmjopen.bmj.com/cgi/content/short/16/9/e110915?rss=1)
- **Published At:** 2026-09-22T17:11:09.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Dyspnoea is a common, distressing symptom in patients with **advanced lung cancer**, negatively affecting quality of life and complicating palliative care. - Traditional assessment and management rely on episodic self-report and may not capture the dynamic, multidimensional nature of **breathlessness**. - **Artificial intelligence (AI)** technologies have potential roles in symptom assessment, remote monitoring, risk prediction and decision support for dyspnoea care. - The scope, data inputs, reported outcomes, and care contexts for AI applied to dyspnoea in advanced lung cancer are currently unclear and warrant mapping. - This work is a scoping review following the **Joanna Briggs Institute (JBI)** methodology and aligned with PRISMA-P for the protocol and PRISMA-ScR for reporting the completed review. - Multiple bibliographic and domain-specific databases will be searched from inception with no date limits, including PubMed/MEDLINE, Embase (Ovid), CINAHL, Web of Science, Scopus, Cochrane Library, IEEE Xplore, ACM Digital Library, PsycINFO and major Chinese databases (CNKI, WanFang, VIP, SinoMed). - Supplementary sources include Google Scholar, ClinicalTrials.gov, WHO ICTRP, medRxiv, arXiv, ProQuest Dissertations & Theses and reference lists of included studies and reviews. - Eligible evidence comprises original quantitative, qualitative and mixed-methods studies and full grey-literature reports with sufficient empirical data; reviews will be used to identify primary studies but not as evidence units. - Two independent reviewers will screen records at title/abstract and full-text stages, with a third reviewer to resolve disagreements if necessary. - Data extraction will use a piloted charting form capturing study characteristics, AI technology type, data sources, dyspnoea-related outcomes, implementation context and feasibility or performance metrics when available. - Findings will be synthesised descriptively and thematically to map AI applications in dyspnoea management across clinical and home-care settings. - No ethical approval is required because only published and publicly available literature will be used; dissemination plans include a peer-reviewed paper, conference presentations and summary briefs for clinicians, researchers and policymakers. - The protocol is registered in PROSPERO (CRD420251112279).
## Clinical Analysis & Structured Key Points
Introduction Dyspnoea is one of the most distressing symptoms in patients with advanced lung cancer, substantially impairing quality of life and complicating palliative care. Traditional assessment and management approaches often rely on episodic self-report and may not fully capture the dynamic and multidimensional nature of breathlessness. Artificial intelligence (AI) technologies may support symptom assessment, remote monitoring, risk prediction and decision support. However, the types of AI technologies used for dyspnoea-related care in advanced lung cancer, the data inputs and outcomes reported and the clinical or home-care contexts in which these tools are applied remain unclear. This scoping review will map the available evidence on AI applications in dyspnoea management for patients with advanced lung cancer. Methods and analysis This review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews. The protocol will be prepared in line with Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) where applicable, and the completed review will be reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We will search PubMed/MEDLINE, Embase (Ovid), CINAHL (EBSCO), Web of Science Core Collection, Scopus, Cochrane Library, IEEE Xplore, ACM Digital Library, PsycINFO, CNKI, WanFang Data, VIP Database and SinoMed from database inception with no date restrictions. Supplementary searches will include Google Scholar, ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform, medRxiv, arXiv, ProQuest Dissertations & Theses and reference lists of included studies and relevant reviews. Eligible evidence will include original quantitative, qualitative and mixed-methods studies as well as full grey-literature reports with sufficient empirical data. Systematic, scoping and narrative reviews will not be included as evidence units but will be used to identify additional primary studies. Two reviewers will independently screen titles, abstracts and full texts, with disagreements resolved by discussion or a third reviewer. Data will be charted using a piloted extraction form and will include study characteristics, AI technology type, data sources, dyspnoea-related outcomes, implementation context and reported feasibility or performance metrics where available. Findings will be synthesised descriptively and thematically. Ethics and dissemination As this review will use published and publicly available literature and will not involve primary patient data, ethical approval will not be required. Findings will be disseminated through a peer-reviewed results paper, conference presentations and summary briefs for clinicians, researchers and policymakers. Registration This scoping review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO, registration number CRD420251112279).
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