This paper presents the results of a scoping review that examines potentials of Artificial Intelligence (AI) in early diagnosis of Cognitive Decline (CD), which is regarded as a key issue in elderly health. The review encompasses peer-reviewed publications from 2020 to 2025, including scientific journals and conference proceedings. Over 70% of the studies rely on using magnetic resonance imaging (MRI) as the input to the AI models, with a high diagnostic accuracy of 98%. Integration of the relevant clinical data and electroencephalograms (EEG) with deep learning methods enhances diagnostic accuracy in the clinical settings. Recent studies have also explored the use of natural language processing models for detecting CD at its early stages, with an accuracy of 75%, exhibiting a high potential to be used in the appropriate pre-clinical environments.

Digital twin technology is a potential transformative tool in heart care, enabling precise modeling of cardiovascular functions. However, current applications predominantly focus on physiological processes and short-term medical interventions, neglecting the integration of long-term lifestyle factors such as diet, physical activity, stress management, and sleep patterns—key contributors to heart health. This paper addresses this critical research gap by proposing a three-level framework for lifestyle-focused digital twin archetypes: basic (utilizing self-reported data for general health guidance), intermediate (incorporating wearable-generated data for dynamic feedback), and advanced (combining comprehensive clinical and lifestyle data for detailed simulations and personalized treatment planning). By integrating lifestyle factors, the framework could enhance personalization, patient engagement, and improve long-term health outcomes. The approach emphasizes multidisciplinary collaboration and paves the way for a holistic understanding of heart health, offering scalable solutions to bridge technology and lifestyle for better cardiovascular care.

The concept of digital twins has emerged as a transformative innovation in healthcare. Digital twins are virtual replicas of physical entities that can be updated with real-time data allowing for simulation analysis and optimization. Their application in healthcare can potentially improve patient care, medical research, and healthcare management. In recent years, there has been an increasing interest in research on digital twins, particularly since 2019, there has been an explosive surge in research, driven by rapid advancements in machine learning, artificial intelligence, and healthcare technologies. Great efforts have been put into defining frameworks and developing applications and outcome-oriented research. There are also promising industrial applications illustrating the feasibility of replicating heart functioning. Intensive research growth is noticeable in academia and clinical research and to some extent within the industry. The results of the literature review done in this study reveal a significant body of academic research and a limited number of industrial applications within the field of heart disease. Understanding of the concept is still evolving, shaped by diverse applications, research objectives, and multidisciplinary collaborations in which digital twins are formulated. Harmonizing definitions and understanding digital twins is essential for ensuring methodological advancement of the concept and fostering multi-disciplinary collaboration leading to reliable and impactful outcomes in research and in practice. 

Early detection of cognitive impairment is crucial for timely intervention in conditions like dementia and Parkinson’s disease. While the Mini-Cog is a validated tool for such screening, its traditional clinical format may disengage some users. This study introduces a gamified, narrative-based version of the Mini-Cog as a web-mobile application, tested with 30 healthy volunteers (ages 45–72) and one patient. Two story variantswere developed, featuring a mayor and a dinosaur, embedded in a relaxed everyday narrative mentioning a park, bakery, and birthday party. Feedback highlights positiveengagement through visuals and icons, with suggestions or age-adapted stories and improved screen adaptation. Results support the feasibility of gamified cognitive screening whi

This paper presents the results of a scoping review that examines potentials of Artificial Intelligence (AI) in early diagnosis of Cognitive Decline (CD), which is regarded as a key issue in elderly health. The review encompasses peer-reviewed publications from 2020 to 2025, including scientific journals and conference proceedings. Over 70% of the studies rely on using magnetic resonance imaging (MRI) as the input to the AI models, with a high diagnostic accuracy of 98%. Integration of the relevant clinical data and electroencephalograms (EEG) with deep learning methods enhances diagnostic accuracy in the clinical settings. Recent studies have also explored the use of natural language processing models for detecting CD at its early stages, with an accuracy of 75%, exhibiting a high potential to be used in the appropriate pre-clinical environments.

Digital twins simulate patient conditions and treatment processes in healthcare, but their interpretation remains a challenge. This study reviews existing literature on visualization tools, specifically dashboards and infographics, and their application in digital twin healthcare systems. Dashboards support healthcare professionals by offering interactive, real-time data, while infographics simplify complex data for public engagement. Through case studies, the paper highlights the visualization principles of both tools. The findings suggest that improved visualization methods are crucial for advancing digital twin adoption in healthcare. 

This research explores how infographics can enhance public understanding of nutrition based on the Norwegian Directorate of Health’s dietary guidelines. Although these guidelines are designed for the entire population, the Directorate has indicated to target younger Norwegians in their outreach efforts. To make the guidelines more accessible across age groups, we transformed the Directorate's text-based content into engaging infographics. Three mid-fidelity prototypes were developed: Infographic 1 emphasizes simplicity with basic illustrations and structured text; Infographic 2 uses a colorful segmented wheel; and Infographic 3 combines detailed illustrations with unboxed text. These designs were evaluated by 72 participants in age-stratified focus groups. Results showed a preference for Infographics 1 and 3, valued for their clarity and visual appeal, with age and gender influencing preferences. Based on participant feedback, revised versions of the infographics were created. The findings confirm that well-designed infographics can significantly boost engagement, interaction, and retention of nutritional information.

Effective data visualization is essential for translating complexinformation into accessible insights that support interpretation and decision-making.This study explores the use of dashboards and infographics in visualizing climatechange concern, using data from the 2023 Norwegian Quality of Life Survey.Infographics offered clear, narrative snapshots for public understanding, whiledashboards enabled interactive exploration of regional and demographic variations.Key predictors of climate concern included attitudes, general worry, andurbanization, with urban residents expressing the most concern. Visualization toolsalso contributed to early-stage digital twin modeling by making abstract datastructures more relatable and actionable. By combining visual formats, this studyenhances user engagement and demonstrates how tailored communication tools canbridge the gap between research and public awareness. Dashboards and infographicsserve complementary roles in health and climate communication, supporting bothexploration and understanding.

Antibiotic stewardship is continuously evolving to incorporate results from novel research, clinical findings, and specialist recommendations. Numerous dedicated information sources, including web-based solutions, are available to keep medical practitioners informed. However, the provided information is often extensive, requiring users to extract the relevant facts. This study aimed to deliver an information platform that provides references, links, and information in a straightforward and engaging manner. Implementing a high-fidelity prototype prioritized medical and patient-oriented functionalities, structured around questions and quizzes. Additionally, the platform offers access to professional references, such as official healthcare guidelines and scientific articles. The development process adhered to design principles and included user testing with established usability measures (SUS, Nielsen's heuristics), resulting in satisfactory scores from IT experts and somewhat lower scores from users. Although designed to cater to a broader range of users, more work is needed to improve usability for the general public.

This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.