Are single-lead consumer ECG devices enough for accurate QTc monitoring? A calibrated uncertainty AI approach.

Background: While single‑lead ECGs offer accessibility, their performance and reliability for QTc assessment remains uncertain. Current State-of-the-art AI systems, though promising, often lack transparency, raising concerns about clinical trustworthiness.

Methods: We developed an uncertainty-aware AI model to measure RR/QT intervals from single‑lead ECGs.

A Virtual Simulator to Improve Weight-Related Communication Skills for Health Care Professionals: Mixed Methods Pre-Post Pilot Feasibility Study.

Background: Discussing weight remains a sensitive and often avoided topic in health care, despite rising prevalence of obesity and calls for earlier, more compassionate interventions. Many health care professionals report inadequate training and low confidence to discuss weight, while patients often describe feeling stigmatized or dismissed. Digital simulation offers a promising route to build communication skills through supporting repeatable and reflective practice in a safe space.

Grouping Digital Health Apps Based on Their Quality and User Ratings Using K-Medoids Clustering: Cross-Sectional Study.

Background: Digital health apps allow for proactive rather than reactive health care and have the potential to take the pressure off health care providers. With over 350,000 digital health apps available on the app stores today, those apps need to be of sufficient quality to be safe to use. Discovering the typology of digital health apps regarding professional and clinical assurance (PCA), user experience (UX), data privacy (DP), and user ratings may help in determining the areas where digital health apps can improve.

Children and Young People's Priorities for Mental Health Research in Northern Ireland.

Introduction: There are a number of factors contributing to the poor mental health of children and young people (CYP) specific to life in Northern Ireland (NI). Prevention and early intervention are of critical importance to the mental health and well-being of CYP. Policy decisions and service provision in the health and education sectors must be informed by research so that we can understand the factors affecting the mental health of young people and develop effective policy responses.

The impact of AI feedback on the accuracy of diagnosis, decision switching and trust in radiography.

Artificial intelligence decision support systems have been proposed to assist a struggling National Health Service (NHS) workforce in the United Kingdom. Its implementation in UK healthcare systems has been identified as a priority for deployment. Few studies have investigated the impact of the feedback from such systems on the end user.

Artificial Intelligence user interface preferences in radiology: A scoping review.

Introduction/background: Modern forms of Artificial intelligence (AI) have developed in radiology over the past few years. With the current workforce shortages, in both radiology and radiography professions, AI continues to prove its place in supporting clinical radiology processes. The aim of the scoping review was to investigate the existing literature on the topic of preference of use of artificial intelligence interfaces within a radiology context.

Assessing the Uses, Benefits, and Limitations of Digital Technologies Used by Health Professionals in Supporting Obesity and Mental Health Communication: Scoping Review.

Background: Obesity and mental health issues present interconnected public health challenges that impair physical, social, and mental well-being. Digital technologies offer potential for enhancing health care communication between health professionals (HPs) and individuals living with obesity and mental health issues, but their effectiveness is not fully understood.

Objective: This scoping review aims to identify and understand the different types of technologies used by HPs in supporting obesity and mental health communication.

Understanding Individual Differences in Happiness Sources and Implications for Health Technology Design: Exploratory Analysis of an Open Dataset.

Background: Psychologists have developed frameworks to understand many constructs, which have subsequently informed the design of digital mental health interventions (DMHIs) aimed at improving mental health outcomes. The science of happiness is one such domain that holds significant applied importance due to its links to well-being and evidence that happiness can be cultivated through interventions. However, as with many constructs, the unique ways in which individuals experience happiness present major challenges for designing personalized DMHIs.

Effectiveness of Digital Mental Health Interventions in the Workplace: Umbrella Review of Systematic Reviews.

Background: There is potential for digital mental health interventions to provide affordable, efficient, and scalable support to individuals. Digital interventions, including cognitive behavioral therapy, stress management, and mindfulness programs, have shown promise when applied in workplace settings.

Objective: The aim of this study is to conduct an umbrella review of systematic reviews in order to critically evaluate, synthesize, and summarize evidence of various digital mental health interventions available within a workplace setting.

Recommendations for Mental Health Chatbot Conversations: An Integrative Review.

Aim: To identify and synthesise recommendations and guidelines for mental health chatbot conversational design.

Design: Integrative review.

Methods: Suitable publications presenting recommendations or guidelines for mental health conversational design were included.

An evaluation of a checklist in musculoskeletal radiographic image interpretation when using artificial intelligence.

Introduction: Artificial intelligence (AI) is being used increasingly in image interpretation tasks. Human reliance on technology and bias can cause decision errors. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene.

Evaluating the Impact of a Daylight-Simulating Luminaire on Mood, Agitation, Rest-Activity Patterns, and Social Well-Being Parameters in a Care Home for People With Dementia: Cohort Study.

Background: Living with a diagnosis of dementia can involve managing certain behavioral and psychological symptoms. Alongside cognitive decline, this cohort expresses a suppression in melatonin production which can negatively influence their alignment of sleep or wake timings with the 24 hour day and night cycle. As a result, their circadian rhythms become disrupted.

Quality Assessment of Digital Health Apps: Umbrella Review.

Background: With an increasing number of digital health apps available in app stores, it is important to assess these technologies reliably regarding their quality. This is done to mitigate the risks associated with their use. There are many different guidelines, methods, and metrics available to assess digital health apps with regard to their quality.

Reporting radiographers' interaction with Artificial Intelligence-How do different forms of AI feedback impact trust and decision switching?

Artificial Intelligence (AI) has been increasingly integrated into healthcare settings, including the radiology department to aid radiographic image interpretation, including reporting by radiographers. Trust has been cited as a barrier to effective clinical implementation of AI. Appropriating trust will be important in the future with AI to ensure the ethical use of these systems for the benefit of the patient, clinician and health services.

Digital transformation of mental health services.

This paper makes a case for digital mental health and provides insights into how digital technologies can enhance (but not replace) existing mental health services. We describe digital mental health by presenting a suite of digital technologies (from digital interventions to the application of artificial intelligence). We discuss the benefits of digital mental health, for example, a digital intervention can be an accessible stepping-stone to receiving support.

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial.

Background: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.

Objective: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.

Don't judge a book or health app by its cover: User ratings and downloads are not linked to quality.

Objective: To analyse the relationship between health app quality with user ratings and the number of downloads of corresponding health apps.

Materials And Methods: Utilising a dataset of 881 Android-based health apps, assessed via the 300-point objective Organisation for the Review of Care and Health Applications (ORCHA) assessment tool, we explored whether subjective user-level indicators of quality (user ratings and downloads) correlate with objective quality scores in the domains of user experience, data privacy and professional/clinical assurance. For this purpose, we applied spearman correlation and multiple linear regression models.

Quality of Digital Health Interventions Across Different Health Care Domains: Secondary Data Analysis Study.

Background: There are more than 350,000 digital health interventions (DHIs) in the app stores. To ensure that they are effective and safe to use, they should be assessed for compliance with best practice standards.

Objective: The objective of this paper was to examine and compare the compliance of DHIs with best practice standards and adherence to user experience (UX), professional and clinical assurance (PCA), and data privacy (DP).

An experimental machine learning study investigating the decision-making process of students and qualified radiographers when interpreting radiographic images.

AI is becoming more prevalent in healthcare and is predicted to be further integrated into workflows to ease the pressure on an already stretched service. The National Health Service in the UK has prioritised AI and Digital health as part of its Long-Term Plan. Few studies have examined the human interaction with such systems in healthcare, despite reports of biases being present with the use of AI in other technologically advanced fields, such as finance and aviation.

Ventricular fibrillation waveform properties influenced by thoracic impedance guided chest compressions in a porcine model.

Background And Objective: Quantitative measures extracted from ventricular fibrillation (VF) waveform reflect the metabolic state of the myocardium and are associated with survival outcome. The quality of delivered chest compressions during cardiopulmonary resuscitation are also linked with survival. The aim of this research is to explore the viability and effectiveness of a thoracic impedance (TI) based chest compression (CC) guidance system to control CC depth within individual subjects and influence VF waveform properties.