Dysphagia, or difficulty in swallowing, significantly impacts the quality of life of the affected individuals. Diagnostic approaches, including video fluoroscopic swallowing studies and flexible endoscopic evaluation of swallowing, are the most commonly used methods for assessing swallowing function. Recent advancements have led to the development of artificial intelligence (AI), including machine learning (ML) and deep learning (DL), which will provide innovative approaches to dysphagia diagnosis and treatment planning. There is a limited synthesis of literature on AI tools in dysphagia. There is an urgent need for a more rigorous and structured scoping review that can address the existing gaps, provide a more comprehensive evidence synthesis, and establish clearer guidelines for the clinical implementation of AI in assessments and management of dysphagia. This review will include studies focusing on AI tools such as ML, DL and computer vision for assessing and managing dysphagia. The context will be clinical or therapeutic settings, and all language articles will be considered for the review. Studies not involving AI technologies, those without clinical outcomes and ethical approval, and those focusing solely on the paediatric population will be excluded. This scoping review will systematically map and synthesise the existing literature on the use of AI tools for the assessment and management of dysphagia.

This scoping review will follow JBI methodology and PRISMA ScR guidelines. Information to be searched from January 2000 to May 2025 will include MEDLINE (via Ovid), Scopus, CINAHL (via EBSCOhost), Cochrane Library, JBI Evidence Synthesis, ProQuest and Google Scholar. The titles, abstracts and full texts will be screened by two independent reviewers. Data extraction will use a study-specific customised form, with descriptive analysis employed to categorise studies by AI tools and outcomes.

Ethical approval is not mandatory for this scoping review as it does not entail the collection of any individual patient data. Secondary data from publicly accessible research papers will be used. All the data sources will be appropriately cited. The findings will be propagated through peer-reviewed publications and scientific presentations.

Open Science Framework: DOI 10.17605/OSF.IO/DYCE9.

Traditional oncology outcome measures, such as survival rates and disease progression, fail to fully capture the complex lived experiences of persons and patients with cancer, including psychological distress, financial burdens and changes in social roles. While person- and patient-centred outcomes have emerged as essential components of quality cancer care, ambiguities persist regarding their definitions and measurement methodologies in clinical trials.

This scoping review aims to explore how person-centred and patient-centred outcomes are defined and measured in cancer clinical trials and to identify trends, gaps and methodological approaches for their assessment. Comprehensive searches will be conducted across PubMed, SCOPUS, Sci-Elo, EMBASE, PsycINFO and Google Scholar for grey literature sources, encompassing articles from August 2020 to August 2025. Eligible studies include primary research that reports patient- or person-centred outcomes in cancer clinical trials. Studies focusing solely on preventative care or lacking assessment of patient- or person-centred outcomes will be excluded. Studies will be independently screened and selected by two reviewers in duplicate, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Data extraction will also be conducted independently and in duplicate using a standardised extraction tool, with disagreements resolved through consensus to ensure consistency and accuracy. Results will be synthesised qualitatively and quantitatively, with narrative and thematic analysis used to identify trends and gaps in the literature.

Ethical approval is not required. Results will be published in a peer-reviewed journal and presented at conferences.

https://doi.org/10.17605/OSF.IO/EYZPK

Community health workers (CHWs) are critical to healthcare delivery in low-resource settings but often lack formal clinical training, limiting their decision-making. Large language models (LLMs) could provide real-time, context-specific support to improve referrals and management plans. This study aims to evaluate the potential utility of LLMs in assisting CHW decision-making in Rwanda.

This is a prospective, observational study conducted in Nyabihu and Musanze districts, Rwanda. Audio recordings of CHW-patient consultations will be transcribed and analysed by an LLM to generate referral decisions, differential diagnoses and management plans. These outputs, alongside CHW decisions, will be evaluated against a clinical expert panel’s consensus. The primary outcome is the appropriateness of referral decisions. Secondary outcomes include diagnostic accuracy, management plan quality, and patient and user perceptions to ambient recording of consultations. Sample size is set at 800 consultations (400 per district), powered to detect a 15–20 percentage point improvement in referral appropriateness.

Ethical approval has been obtained from the Rwandan National Ethics Committee (RNEC) (Ref number: RNEC 853/2025) in June 2025, recruitment started in July 2025 and results are expected in late 2025. Results will be disseminated via stakeholder meetings, academic conferences and peer-reviewed publication.

PACTR202504601308784.

This study aimed to analyse the number of myocardial infarction (MI) admissions during the COVID-19 lockdown periods of 2020 and 2021 (March 15th to June 15th) and compare them with corresponding pre-pandemic period in 2019. The study also evaluated changes in critical treatment intervals: onset to door (O2D), door to balloon (D2B) and door to needle (D2N) and assessed 30-day clinical outcomes. This study examined MI care trends in India during the COVID-19 lockdown period, irrespective of patients’ COVID-19 infection status.

Multicentre retrospective cohort study

Twenty-three public and private hospitals across multiple Indian states, all with 24/7 interventional cardiology facilities.

All adults (>18 years) admitted with acute myocardial infarction between March 15 and June 15 in 2019 (pre-pandemic), 2020 (first lockdown) and 2021 (second lockdown). A total of 3614 cases were analysed after excluding duplicates and incomplete data.

Number of MI admissions, median O2D, D2B and D2N times.

30-day outcomes including death, reinfarction and revascularisation.

MI admissions dropped from 4470 in year 2019 to 2131 (2020) and 1483 (2021). The median O2D increased from 200 min (IQR 115–428) pre-COVID-19 to 390 min (IQR 165–796) in 2020 and 304 min (IQR 135–780) in 2021. The median D2B time reduced from 225 min (IQR 120–420) in 2019 to 100 min (IQR 53–510) in 2020 and 130 min (IQR 60–704) in 2021. Similarly, D2N time decreased from 240 min (IQR 120–840) to 35 min (IQR 25–69) and 45 min (IQR 24–75), respectively. The 30-day outcome of death, reinfarction and revascularisation was 4.25% in 2020 and 5.1% in 2021, comparable to 5.8% reported in the Acute Coronary Syndrome Quality Improvement in Kerala study.

Despite the expansion of catheterisation facilities across India, the country continues to fall short of achieving international benchmarks for optimal MI care.