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Mapping the AI Landscape in Dyspnoea Management for Advanced Lung Cancer: A New Scoping Review Protocol
Key Facts
| Protocol Registration | Methodology | Search Scope | Target Population |
|---|---|---|---|
| PROSPERO CRD420251112279 PMID: 42772888 | Joanna Briggs Institute (JBI) PMID: 42772888 | 13 primary databases + grey literature PMID: 42772888 | Patients with advanced lung cancer PMID: 42772888 |
Why It Matters / Context
Dyspnoea is widely recognized as one of the most distressing symptoms experienced by patients with advanced lung cancer, substantially impairing quality of life and complicating palliative care delivery PMID: 42772888. Traditional clinical approaches to managing breathlessness often rely on episodic self-reporting, which may fail to capture the dynamic, multidimensional, and fluctuating nature of the symptom PMID: 42772888.
The integration of Artificial Intelligence (AI) offers a potential paradigm shift in symptom management. AI technologies can support continuous symptom assessment, remote monitoring, risk prediction, and clinical decision support PMID: 42772888. However, despite the rapid advancement of digital health tools, the specific types of AI technologies being used for dyspnoea-related care in advanced lung cancer remain unclear PMID: 42772888. Furthermore, there is a lack of clarity regarding the data inputs required, the outcomes reported, and the clinical or home-care contexts in which these tools are applied PMID: 42772888.
This protocol addresses a significant gap in the literature by proposing a comprehensive scoping review to map the available evidence PMID: 42772888. By identifying the current state of AI applications, the review aims to provide a foundation for future research and clinical implementation strategies PMID: 42772888. The study is particularly relevant as the oncology community increasingly looks toward digital therapeutics to enhance palliative care outcomes without increasing the burden on patients or healthcare systems PMID: 42772888.
Details
The review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews, with the protocol prepared in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) PMID: 42772888. The completed review will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) PMID: 42772888.
Search Strategy and Data Sources
The search strategy is extensive, covering 13 primary databases from inception with no date restrictions: 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 PMID: 42772888. 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 PMID: 42772888.
Eligibility Criteria
Eligible evidence includes original quantitative, qualitative, and mixed-methods studies, as well as full grey-literature reports with sufficient empirical data PMID: 42772888. Systematic, scoping, and narrative reviews will not be included as evidence units but will be used to identify additional primary studies PMID: 42772888.
Data Extraction and Synthesis
Two reviewers will independently screen titles, abstracts, and full texts, with disagreements resolved by discussion or a third reviewer PMID: 42772888. Data will be charted using a piloted extraction form that includes:
- Study characteristics
- AI technology type
- Data sources
- Dyspnoea-related outcomes
- Implementation context
- Reported feasibility or performance metrics
Findings will be synthesized descriptively and thematically PMID: 42772888.
Limitations
Critical Note: This publication is a protocol for a scoping review, not the review itself. It outlines the plan for evidence synthesis but does not present the results, findings, or conclusions of the review. No data on specific AI performance metrics, clinical outcomes, or patient experiences are available in this document. The actual impact of AI on dyspnoea management remains to be determined by the completed review. Furthermore, the inclusion of grey literature and preprints (medRxiv, arXiv) may introduce variability in the quality and rigor of the included studies, which the authors acknowledge by requiring “sufficient empirical data” PMID: 42772888.
Clinical Takeaway
Clinicians and researchers should monitor the completion of this scoping review (PROSPERO CRD420251112279) to identify validated AI tools for dyspnoea monitoring in advanced lung cancer, as current evidence on the specific types of technologies and their clinical contexts remains fragmented and unclear.
Full Reference
Zhang X, Wang L, Sun Y, Zhang W, Zheng X. Applications of artificial intelligence in dyspnoea management for patients with advanced lung cancer: a scoping review protocol. BMJ Open. 2026. PMID: 42772888 [DOI: 10.1136/bmjopen-2025-110915]