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Applications of artificial intelligence in dyspnoea management for patients with advanced lung cancer: a scoping
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).