BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet the attitudes and knowledge of nurses, who are the key mediators of AI implementation, remain underexplored. This study aimed to evaluate the psychometric properties of a previously validated nine-item scale measuring nurses’ knowledge and attitudes toward AI and to describe preliminary findings from primary healthcare centre […]
Identifying needs in adult rehabilitation to support the clinical implementation of robotics and allied technologies: an Italian national survey
IntroductionRobotics and technological interventions are increasingly being explored as solutions to improve rehabilitation outcomes but their implementation in clinical practice remains very limited. Understanding patient needs is crucial for effective integration of these technologies, ensuring they align with and address the actual requirements of individuals in clinical settings. The primary aim of this study is […]
Ethical examination of AI coaches: privacy, bias, and responsibility
The integration of artificial intelligence (AI) into sports, particularly through AI-driven coaching systems, marks a transformative advancement with the potential to revolutionize personalized training. AI coaches can create customized, data-driven training programs designed to optimize athletic performance. However, this technological progress also brings with it significant ethical concerns, including privacy violations, data biases, and ambiguous […]
Evaluating privacy leakages in LLM-driven ambient clinical documentation
IntroductionAutomated documentation tools are being rapidly adopted in healthcare and clinical workflows. Among these are AI-enabled ambient scribing products, which transcribe conversations between patients and healthcare providers, then produce clinical records using automatic speech recognition (ASR) and generative AI such as Large Language Models (LLMs). While research suggests these technologies can reduce clinical burden, safe […]
Assessing ChatGPT vs. evidence-based online responses for polycystic ovary syndrome self-management and education: an international cross-sectional blinded survey of healthcare professionals
Artificial intelligence (AI)-powered large language models, such as ChatGPT, are increasingly used by the public for health information. The reliability of such novel AI-tools in providing credible polycystic ovary syndrome (PCOS) information/advice requires investigation. Healthcare professionals involved in PCOS care (n = 43 from 14 countries) used a 5-point Likert scale to evaluate ChatGPT-generated responses to frequently […]
Single-item measures in digital mental healthcare: a mini narrative review of challenges and opportunities
Single-item measures (SIMs) are increasingly used by digital mental health services for assessment, outcome monitoring, and population-level surveillance. Their simplicity offers clear advantages, including good face validity, practical efficiency, and the potential to integrate across digital platforms. However, concerns persist regarding their reliability and suitability for complex psychological constructs. This mini narrative review synthesises recent […]
Evaluating the quality of online patient education materials for gastric adenocarcinoma
BackgroundGastric adenocarcinoma, or gastric cancer, typically has a poor prognosis. The objective of this study was to assess the quality, understandability, actionability, and comprehensiveness of online resources for patients diagnosed with gastric adenocarcinoma, or gastric cancer as patients increasingly rely on online health information.MethodsA systematic search using the term “stomach cancer” was conducted across three […]
A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction
The exponential growth of biomedical data necessitates advanced tools for efficient information extraction (IE) to support clinical decision-making and research. Large language models (LLMs) have emerged as transformative solutions, yet their application in healthcare raises critical trade-offs between open-source (OSS) and proprietary models. This review evaluates IE workflows such as named entity recognition, relation extraction, […]
Deploying medical AI in low-resource settings: a scoping review of challenges and strategies
BackgroundArtificial intelligence (AI) is increasingly used to enhance diagnostic accuracy, clinical decision-making, and health system efficiency. However, its sustainable and equitable deployment in low-resource settings (LRS) remains limited. In many low- and middle-income countries (LMICs), digital health efforts are still held back by weak infrastructure, fragmented health data, limited local skills, and gaps in governance. […]
Use of digital patients in clinical simulation for nursing education: a scoping review protocol
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 […]
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 […]
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 […]