IntroductionClinical simulation represents an emerging educational technology delivered through software-based platforms, accessible via computers or head-mounted displays. It is characterized as a partially immersive, screen-mediated experience in which learners are placed in simulated roles that require the execution of psychomotor actions, clinical decision-making, and interpersonal communication skills.Methods and analysisThis scoping review protocol follows the methodological […]
Structuring medication safety narratives: development and evaluation of the medication-related incident reports annotation scheme
IntroductionNarrative reports of medication-related incidents contain valuable information about the causes and consequences of errors, but their unstructured format limits systematic analysis. Although natural language processing (NLP) can convert narrative reports into structured data, few annotation schemes have been developed specifically for medication safety and validated using real-world healthcare incident data. This study aimed to […]
From memorization to generalization: fine-tuning large language models for biomedical term-to-identifier normalization
IntroductionBiomedical data integration requires term-to-identifier normalization, the process of linking natural-language biomedical terms to standardized ontology codes so that extracted concepts become computable and interoperable. Although large language models perform well on clinical text summarization and concept extraction, they remain markedly less accurate at mapping ontology terms to their corresponding identifiers.MethodsWe examined the roles of […]
Influence of social media on cosmetic and gynecologic aesthetic decisions among women in Saudi Arabia
ObjectiveTo explore how social media influences cosmetic and gynecologic aesthetic decision-making among women in Saudi Arabia, with attention to behavioral, psychological, ethical, and cultural dimensions.MethodsA structured narrative review guided by SANRA principles was conducted using publications from 2018 to 2024 identified through PubMed, OpenAlex, and institutional repositories. Studies were screened thematically and synthesized using a […]
Amplifying missing voices in healthcare research: an AI framework for co-production of PPIE
Patient and Public Involvement and Engagement (PPIE) is essential for high-quality healthcare research, yet significant challenges persist in achieving diverse input. Traditional PPIE panels can struggle with recruitment limitations, geographical constraints, and resource intensity, resulting in panels that may not reflect population diversity or lived experiences. In order to address these challenges, we developed Panelyze, […]
IoT-based health monitoring and social welfare access for Thailand’s older adults
IntroductionThailand’s population is shifting, with 20% aged 60 and over in 2022, leading to healthcare and social welfare challenges. Digital technologies, particularly those using IoT, may improve health outcomes for older adults but face hurdles due to low digital literacy and a digital divide between urban and rural areas. This study investigated the accessibility and […]
Federated learning for fair autism spectrum disorder screening across age-heterogeneous populations
IntroductionThe detection of Autism Spectrum Disorder (ASD) remains challenging due to the heterogeneity of behavioural manifestations, limited dataset availability, and strict privacy requirements. Conventional centralized machine learning approaches often suffer from overfitting and limited generalizability across different age groups. This study proposes a federated learning (FL) framework to enable collaborative ASD screening across children, adolescents, […]
Advancing the adoption of oncology decision support tools in Europe: insights from CAN.HEAL
Effective cancer care increasingly depends on digital decision support tools (DSTs) to interpret complex clinical, molecular, and genomic data and guide personalised treatment decisions. However, the oncology DST (oncDST) landscape remains fragmented, with limited interoperability, inconsistent standards, and uneven clinical adoption across healthcare systems. This fragmentation hinders routine clinical use and impedes the demonstration of […]
Enhanced meta ensemble stacking approach with XGBoost and optuna based detection of Parkinson’s disease
Parkinson’s disease (PD), a progressive neurological disorder affecting motor function, has been significantly rising in prevalence in recent years. Current diagnostic methods, relying on clinical observations, neurological exams, and periodical DaTscan imaging, may exhibit reduced sensitivity in the early stages. To develop a robust and multimodal machine learning model for early detection, an Ensemble Approach […]
Early Type 2 diabetes risk prediction using explainable machine learning in a two-stage approach
BackgroundDiabetes is a chronic disease characterized by elevated blood glucose levels. Without early detection and proper management, it can lead to serious complications and increase healthcare costs. Its global prevalence is rising, with many cases remaining undiagnosed. In this study, we developed an explainable machine learning model using a two-stage approach for predicting diabetes.MethodsFive machine […]
Practical templates for digital health ethics applications in Sweden: lessons from a sensor-based monitoring study
Obtaining ethical approval for digital health research involving vulnerable populations presents significant challenges for researchers, particularly when navigating complex regulatory frameworks like Sweden’s ethical review system. Despite official guidelines, researchers often struggle to translate general principles into concrete application documents that satisfy review authorities. This paper presents practical, reusable templates developed through the successful preparation […]
Partial health status observability and time horizon uncertainty in mean-field game epidemiological models$^*$
arXiv:2604.04305v1 Announce Type: cross Abstract: We introduce Mean-Field Game (MFG) epidemiological models, in which immunity either wanes with time in a fully observable way or disappears instantaneously with no direct observation (making a previously recovered individual fully susceptible again without realizing it). Both interpretations create computational challenges for rational noninfected individuals deciding on their contact […]