Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in the diagnosis of various diseases, including tropical fevers such as dengue and malaria. However, there is a lack of standard guidelines to develop the AI models, the gap between clinical and engineering expertise and clinical validation of the models, and hence there is a critical need for the development of an integrated diagnostic tool which uses demographical, laboratory variables and epidemiological parameters of patient and provides early prediction.

The present study aimed to develop and evaluate a machine-learning (ML) prediction tool for differential diagnosis of tropical fevers for adult patients (>18 years) using a three-phase approach in a tertiary care centre in South India by January 2026. Phase involves identification of the prevalent tropical fevers and associated clinical parameters to develop the AI model through a retrospective audit and qualitative interview. Phase Ⅱ involves retrospective data collection from hospital medical records for finalised diseases (1000 cases per disease) and clinical parameters, with data being used for model development using the Python language. Support vector machine, logistic regression, K-Nearest Neighbors, Naïve Bayes and ensemble models such as decision tree and Random Forest will be employed along with explainable AI techniques. They are used as they are easy to understand and interpret, well established, most effective for structured data, enhancing the transparency and interpretability of the predictive machine learning models, and their use has been widely supported in previous studies across various contexts. Suitable statistical parameters like specificity, sensitivity and area under receiver operating characteristic (AUROC) will be applied to evaluate model performance. In phase , the developed model will be implemented prospectively to assess the feasibility of model implementation. Model performance such as specificity, sensitivity and AUROC will be calculated, and the finally developed model will be implemented in a single tertiary care hospital to evaluate its overall performance.

Ethical approval for the study has been obtained from the institutional ethics committee of the Kasturba Medical College and Kasturba Hospital, Manipal (IEC number: 6/2024). Informed consent will be taken for obtaining the data of the patient for the evaluation of the model in the third phase of the study, and data will be kept confidential. The study results will be disseminated by publishing them in a peer-reviewed journal.

The protocol has been registered with the Clinical Trial Registry of India (CTRI) (CTRI/2024/04/065866) and approved on 16 April 2024.

To determine the risk perception, health-related adaptive behaviours and associated factors related to climate change among high school students in Thiruvananthapuram district, Kerala, India.

A cross-sectional study with multistage cluster sampling was conducted among high school students from Neyyatinkara Taluk in the Thiruvananthapuram district of Kerala, India. After identifying the taluk, 10 schools were chosen using probability proportionate to size to ensure adequate representation.

The study was conducted among 600 high school students (mean age 14 years, SD 0.75) from Neyyatinkara Taluk in the Thiruvananthapuram district of Kerala.

Neyyattinkara taluk was randomly selected from the six taluks in Thiruvananthapuram district. From each of the 10 selected schools, students from classes 8 to 10, section A, formed the study clusters, with cluster sizes ranging from 45 to 60 students. All students from classes 8 to 10 (section A) who were present on the day of the survey and had obtained informed consent from their parents or guardian were considered eligible to participate in the study. Risk perception and health-related adaptive behaviour scores for children were calculated using a pretested structured questionnaire with 8 and 17 questions, respectively. All questions were designed on a 5-point scale. For positively worded questions, scores ranged from 5 to 1 (strongly agree to strongly disagree), and for negatively worded questions, the scoring was reversed. Binary logistic regression analysis was used to determine the independent factors associated with risk perception and health-related adaptive behaviour.

Nearly three in four study participants (72.1%) were aware of the term climate change. The median risk perception score and health-related adaptive behaviour scores were 28 (IQR 26–30) and 52 (IQR 47–57), respectively. Study participants from urban areas had significantly better risk perception compared with rural counterparts (AOR 2.42; 95% CI 1.54 to 3.78). Similarly, children from above poverty line (APL) households demonstrated markedly higher risk perception than those from below poverty line households (AOR 28.77; 95% CI 16.84 to 45). Participation in a climate change awareness programme was also associated with higher risk perception (AOR 1.98; 95% CI 1.23 to 3.19). Positive health-related adaptive behaviour was more likely among children aged 14–16 years compared with those younger than 14 (AOR 1.92; 95% CI 1.3 to 2.84). Urban residence (AOR 20.72; 95% CI 5.04 to 85.17), higher paternal education (AOR 1.91; 95% CI 1.15 to 3.13) and APL household status (AOR 2.50; 95% CI 1.57 to 3.93) were also significantly associated with better adaptive behaviour.

Climate change interventions and awareness programmes should prioritise rural, lower socioeconomic and younger populations and equip them with practical life skills for adaptive behaviour.

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.

This study sought to explore decision making among caregivers of children with cancer in Pakistan, one of the largest lower middle-income countries in the world.

Cross-sectional survey study

This study was conducted in Pakistan at Indus Hospital and Health Network in Karachi and Children’s Hospital of Lahore. Children’s Hospital of Lahore is a public sector hospital, and Indus Hospital has a foundation-based funding structure. Both are larger tertiary care centers. Over 2,500 new patients are seen at these centers annually, this accounts for almost 50% of all children with cancer in Pakistan

Eligible participants included bedside caregivers, defined as a parent or family member involved in communication with the medical team, of children with cancer (

Primary outcome measures included caregiver priorities and experiences related to communication including decision-making role, involvement of the paediatric patient and decisional regret.

Participants included 200 caregivers of children

Findings from this study highlight the importance of exploring preferences for decision making and empowering bedside caregivers while respecting cultural norms. In the Pakistani context, it may be specifically important to consider gender roles and the inclusion of extended family members. Future work should investigate paediatric patient involvement in diverse settings.

Giant cell arteritis (GCA) is a large-vessel vasculitis occurring in people aged over 50 years. Recent studies have shown that tocilizumab (TCZ), an anti-IL-6 receptor monoclonal antibody, is remarkably effective in treating GCA and allows significant dose sparing of glucocorticoids. However, it makes it difficult to monitor disease activity. Furthermore, treatment is often prolonged over 1 year due to the fear of relapse after stopping TCZ and/or the absence of an optimal discontinuation scheme.

This study aims at comparing two discontinuation regimens in a population of GCA patients who have been treated with TCZ for 12–36 months and have discontinued glucocorticoids for at least 12 weeks. Patients will be randomised with a 1:1 ratio between two arms: immediate discontinuation (cessation) versus gradual discontinuation of TCZ (162 mg subcutaneously every 2 weeks for 12 weeks and then every 4 weeks for 12 additional weeks). Patients will be followed up for 78 weeks. The primary endpoint is relapse-free survival after 26 weeks of follow-up. A total of 120 patients will be randomised (60 in each group) for a period of 3 years.

The trial was approved by an independent ethics committee (CPP Sud Ouest et Outre Mer IV) and the French health authority (French National Agency for Medicines and Health Products Safety—ANSM) through the Clinical Trials Information System (CTIS) provided by the European Medicines Agency (EMA). The informed consent complies with the ICH GCP guideline and regulatory requirements. Eligible patients may only be included in the study after providing informed consent. Findings will be published in peer-reviewed journals and conference presentations.

NCT06037460.

Social isolation and loneliness are prevalent among older adults and associated with negative health outcomes. Virtual reality (VR) interventions have emerged as a potential approach to address this problem, but their effectiveness remains unclear. This systematic review aims to synthesise evidence on the effects of VR interventions on social isolation and loneliness in adults aged 60 years and older.

We will search PubMed, Web of Science, Embase, CINAHL, MEDLINE, The Cochrane Library, PsycINFO and Scopus from inception to February 2025 for randomised controlled trials, quasi-experimental studies and before-after studies that evaluate VR interventions compared with usual care, wait-list, no treatment or other active interventions in older adults. The primary outcomes will be measures of social isolation and loneliness assessed with validated scales. Secondary outcomes will include depression, quality of life, cognitive function, physical function and adverse events. Two reviewers will independently screen, select and extract data from studies. Risk of bias will be evaluated using the Cochrane Risk of Bias Tool 2 for randomised trials and ROBINS-I for non-randomised studies. If feasible, meta-analysis will be performed; otherwise, a narrative synthesis will be conducted. The quality of evidence will be assessed using GRADE.

Ethical approval is not required for this systematic review, as it will only include published data. The review findings will be disseminated through a peer-reviewed publication and conference presentations.

CRD42025637230.

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.

Artificial intelligence (AI) technologies are increasingly being developed and deployed to support clinical decision-making, care delivery and patient monitoring in healthcare. However, the adoption of AI-driven solutions by nurses, who comprise the largest segment of the healthcare workforce and are central to patient care, has been limited to date. Understanding nurses’ perceptions of barriers and facilitators to AI adoption is critical for successful integration of AI in nursing practice. This systematic review aims to identify, appraise and synthesise qualitative evidence on nurses’ perceived barriers and facilitators to adopting AI-driven solutions in their clinical practice.

We will conduct systematic searches across eight electronic databases (PubMed, Web of Science, Embase, CINAHL, MEDLINE, The Cochrane Library, PsycINFO and Scopus) from inception to January 2025, supplemented by hand-searching reference lists and grey literature. Primary qualitative studies and qualitative components of mixed-methods studies exploring licensed/registered nurses’ perceptions of AI adoption in clinical settings will be included. Two independent reviewers will screen studies, extract data using standardised forms and assess methodological quality using the Critical Appraisal Skills Programme checklist. We will employ meta-ethnography to synthesise the qualitative evidence, involving systematic comparison and translation of concepts across studies to develop overarching themes and a theoretical framework. The Grading of Recommendations Assessment, Development and Evaluation Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach will be used to assess confidence in review findings. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines and the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement.

No ethical approval is required as this systematic review will synthesise data from published studies only. The findings will provide valuable insights to inform the development, implementation and evaluation of nurse-oriented strategies for AI integration in healthcare delivery. Results will be disseminated through peer-reviewed publication, conference presentations and stakeholder engagement activities.

CRD42024602808.

While survival rates following neonatal surgery for congenital heart disease (CHD) have improved over the years, neurodevelopmental delays are still highly prevalent in these patients. After correcting for the CHD subtype, the severity of developmental impairment is dependent on multiple factors, including intraoperative brain injury, which is more frequent and more severe in those undergoing aortic arch repair with deep hypothermic circulatory arrest (DHCA). It is proposed that brain injury may be reduced if cooling is stopped at the point of electrocerebral inactivity (ECI) on electroencephalogram (EEG), but there is limited evidence to support this as few centres perform perioperative EEG routinely. This study aims to assess the feasibility of EEG monitoring during neonatal aortic arch repair and investigate the relationship between temperature and EEG to inform the design of a future clinical trial.

Single-centre prospective observational cohort study in a UK specialist children’s hospital, aiming to recruit 74 neonates (≤4 weeks corrected age) undergoing aortic arch repair with DHCA. EEG will be acquired at least 1–3 hours before surgery, and brain activity will be monitored continuously until 24 hours following admission to intensive care. Demographic, clinical, surgical and outcome variables will be collected. Feasibility will be measured by the number of patients recruited, data collection procedures, technically successful EEG recordings and adverse events. The main outcomes are the temperature at which ECI is achieved and its duration, EEG patterns at key perioperative steps and neurodevelopmental outcomes at 24 months postsurgery.

The study was approved by the Yorkshire and The Humber Sheffield National Health Service Research Ethics Committee (20/YH/0192) on 18 June 2020. Written informed consent will be obtained from the participant’s parent/guardian prior to surgery. Findings will be disseminated to the academic community through peer-reviewed publications and presentations at conferences. Parents/guardians will be informed of the results through a newsletter in conjunction with local charities.