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 […]
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 […]
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 […]
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 […]
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 […]
Editorial: Advancing digital mental health for youth
Post Content
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 […]
Fatal deception: how generative AI fosters therapeutic misconception in vulnerable users
Post Content
Cybersecurity breaches in medical devices: analyzing FDA safety communications in response to patient security concerns
IntroductionThe increasing integration of connected medical devices and internet of things (IoT) technologies in healthcare has significantly improved patient care and operational efficiency. However, this rapid digital transformation has also introduced serious cybersecurity vulnerabilities in medical devices, posing risks to patient safety and sensitive health data. Cybersecurity threats can allow unauthorized remote access to devices, […]
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 […]
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 […]