Autism Spectrum Disorder (ASD) is characterized by social interaction difficulties and communication difficulties. Moreover, children with ASD often suffer from other co-morbidities, such as anxiety and depression. Finding appropriate treatment can be difficult as symptoms of ASD and co-morbidities often overlap. Due to these challenges, parents of children with ASD often suffer from higher levels of stress. This research aims to investigate the feasibility of empowering children with ASD and their parents through the use of a serious game to reduce stress and anxiety and a supporting parent application. The New Horizon game and the SpaceControl application were developed together with therapists and according to guidelines for e-health patient empowerment. The game incorporates two mini-games with relaxation techniques. The performance of the game was analyzed and usability studies with three families were conducted. Parents and children were asked to fill in the Spence’s Children Anxiety Scale (SCAS) and Spence Children Anxiety Scale-Parents (SCAS-P) anxiety scale. The game shows potential for stress and anxiety reduction in children with ASD.

Background: Building on the assertion that nearly every clinician will eventually use artificial intelligence (AI), this study provides a triangulated qualitative analysis of the requirements, challenges, and prospects for integrating AI into routine health care practice. This skills gap contributes to cautious and uneven adoption across clinical settings. Despite advancements, many health care professionals report a self-perceived lack of proficiency in comprehending, critically evaluating, and ethically deploying AI tools, which contributes to cautious adoption in clinical settings. Objective: While addressing key research questions, the study investigates the necessary prerequisites, barriers, and opportunities for AI adoption and specific training priorities that medical staff require. The study is uniquely focused on the health care workforce, moving beyond the predominant emphasis in the literature on medical students. Methods: Situated in Flanders, Belgium, a recognized innovation leader but with moderate lifelong learning participation, this research combines 15 semistructured expert interviews, a regional survey of 134 health care professionals, and 3 co-interpretive focus groups with 39 stakeholders, all conducted in 2024. Results: The results expose small generational and mainly occupational divides. For instance, 85.07% (114/134) of survey respondents expressed interest in introductory AI courses tailored to health care, while 80% (134) of them sought practical, job-relevant AI skills. However, only 13.8% (134) of clinicians felt that their training adequately prepared them for AI integration. Notably, younger professionals (50 years of age) prioritized reducing administrative burden. Physicians and dentists reported higher self-assessed AI knowledge, whereas nurses and physiotherapists showed the lowest familiarity. The survey also revealed differences in preferred learning formats, with doctors favoring flexible, asynchronous learning and nurses emphasizing the need for accredited, employer-supported training during work hours. Ethics, though emphasized in academic literature, ranked low in training interest among most practitioners, except for younger and palliative care professionals. Focus group participants confirmed the need for clear regulatory guidance and access to accredited, practically oriented training. A significant insight was that nurses often lacked institutional support and funding for training, despite their pivotal role in AI-enabled workflows. Conclusions: Taken together, these findings indicate that a one-size-fits-all approach to AI education in health care is unlikely to be effective. By triangulating insights across research stages, this study highlights the need for occupation-specific, accessible, and accredited AI training programs that bridge gaps in digital literacy and align with practical clinical priorities. The qualitative insights obtained can inform policy and training priorities in light of the European Union (EU) AI literacy mandates, while highlighting persistent gaps in workforce preparation.

One of the most frequent health problems is stress. It has been linked to negative effects on employee well-being in many occupations, and it is considered responsible for many physical and psychological problems. Traditional in-person relaxation therapy has proven to be effective in reducing stress. However, it has some drawbacks such as high cost, required infrastructure and the need for qualified trainers. Relaxation therapy in Virtual Reality (VR) tries to solve these problems. However, one aspect has received little attention, that is personalised therapy. Indeed, while many studies show the need for patient-tailored relaxation exercises, little existing work focuses on personalised VR content. One reason for this is the complexity of recognising emotions, which is required for emotion-based adaptive VR. In this work, a method for adapting VR content to the emotional state of the user is presented. This model has been applied in a VR relaxation therapy application, which adapts to the user’s emotional state utilising a heuristic optimiser. Simulations have proven the performance and usability of the emotion model. Additionally, this paper explores the impact of the order in which adaptations are performed on the effectiveness of the relaxation experience.