Background: Women face unique and diverse mental health challenges and therefore require tailored interventions that address their specific needs and lived realities. Research has shown a lack of digital interventions designed for and with women from marginalized backgrounds, such as minority racial/ethnic backgrounds or with low socioeconomic status. If Digital Mental Health Interventions (DMHI) are not designed with active involvement of women from diverse backgrounds, they may fail to reach their public health objectives, which potentially exacerbates existing health disparities. How Human-Centered design methods are applied and reported, and whether women from diverse backgrounds are involved in the design and development of Digital Mental Health Interventions (DMHI), is under-researched. Objective: This study examined the methods and frameworks used in the design and development processes of DMHIs, whether and how studies include women from diverse backgrounds, and whether they tailor to their diverse needs related to intersectional identities (e.g., race/ethnicity, socioeconomic status, age) in the design and development process. Methods: We conducted a scoping review following the PRISMA-ScR guidelines. The databases included are Scopus, PubMed and IEEE Xplore, and the databases are searched from the inception until the 31st of May 2026. We included 77 articles that described the design process of digital mental health solutions for women. Results: Among the 77 reviewed studies, 18 (23.4%) did not explicitly state their design methods, and most of them provided limited information on the characteristics of the included populations. Only 16 (20.8%) studies consistently involved users across all design stages. Intersectional identities were considered in only 26 (33.8%) studies. We observed an overrepresentation of research in higher-income countries, an underrepresentation of women from racial/ethnic minority backgrounds, a narrow age range of participants, and a lack of consideration of intersectional identities. Conclusions: Our findings reveal critical gaps in the development of DMHIs for women, including the superficial reporting and application of Human-Centered Design methods in the design process, limited user involvement, and a lack of consideration of diversity, inclusivity and intersectionality. Future research should emphasize active involvement of end-users from the earliest design phases onwards and adopting an intersectional lens in their design processes. We propose a research agenda for better reporting and applying HCD methods in future DMHI research, towards designing diverse, inclusive and equitable digital solutions for all women. Registry and registry number for systematic reviews or meta-analyses: A protocol of this study is pre-registered at Open Science Framework (DOI 10.17605/OSF.IO/WC79P). ... Read more

Artificial intelligence (AI) based mental health apps, especially chatbots, are increasingly being developed for youth, but rarely with their input, especially that of marginalised groups. This results in the development of apps that have low engagement and pose safety concerns. We use participatory methods to explore the preferences and design requirements of youth who face social exclusion, come from migrant backgrounds, or have low socioeconomic positions. We recruited 64 youths from youth work programs around the Netherlands and carried out 6 workshops. The first three explored the use of apps and large language models (LLMs) for well-being, while the last explored youth’s preferences for an LLM chatbot. Data was analysed thematically. Our results showed participants were open to using apps, preferring multifunctional apps, and identified human connection, self-development, and education as potential functions. However, they were reluctant to use chatbots, perceiving them as fake and lacking emotional intelligence. Instead, participants saw chatbots as providers of information, favouring shorter outputs with simple language, although they disagreed on how human-like chatbots should sound. Finally, the need for personalisation was emphasised, showing a desire for control with extensive customisation settings and clear privacy policies. Further work must be done to explore other relevant stakeholders’ views. ... Read more

Machine learning algorithms show promise in assisting clinical decision-making; however, only a few have been successfully implemented in practice. To bridge this gap, it is essential to analyse the clinicians’ perspective on the compatibility of Artificial Intelligence-based clinical decision support systems (AI-CDSSs) with their clinical tasks. We therefore conducted a literature review of 21 empirical qualitative studies that examined the interaction between health professionals and AI-CDSSs. We synthesised the research through the lens of the Task-Technology Fit (TTF) model, analysing task, technology and individual characteristics of AI-CDSS applications, to identify design elements that are (mis)aligned with clinicians’ needs. Three key findings emerged from our analysis. First, clinicians often expressed scepticism about the clinical judgements of AI-CDSSs, particularly questioning the system’s ability to compete with clinical expertise in the absence of contextual information. Users valued AI primarily for specific strengths, such as identifying trends in patient trajectories, consolidating large datasets and pattern recognition, and comparing similar patient cases, but were hesitant to rely on it for clinical decisions. Second, actionability emerged as a desired characteristic of AI-CDSSs. For instance, clinicians particularly appreciated features of AI-CDSSs that enabled them to explore how different clinical actions might influence outcomes, as well as Explainable AI for identifying modifiable variables that impacted prediction scores, allowing them to take informed action. Third, we identified various ways AI-CDSSs could be used in clinical practice, including for patient prioritisation, patient monitoring, care acceleration, risk communication and workflow efficiency. In essence, AI-CDSSs functioned either as an alert system, preventing oversights, or as a tool for more informed decision making. Our analysis challenges the assumption that AI-CDSSs add little value when clinicians disregard its predictions, as it frequently prompts them to critically reassess their judgments through additional testing, consultation with colleagues, and other actions. Overall, our findings underscore the importance of an in-depth understanding of how AI-CDSSs are used in clinical practice. To optimise for effectiveness, the design of AI-CDSSs should prioritise supporting clinicians’ cognitive processes and information needs. This approach ensures that we move beyond the hype, focusing on the responsible integration of AI-CDSSs, and ultimately enhancing patient care. ... Read more

Machine learning-based artificial intelligence (AI) chatbots are increasingly used to promote health and encourage individuals to adopt healthier behaviors. Chatbots driven by generative AI (genAI) simulate human interactions through text or voice to generate personalized content with guidance on topics such as smoking cessation, nutrition, managing stress, and sleep improvement. The use of AI chatbots for health promotion and wellness has been growing since 2023. While empirical evidence suggests their effectiveness in supporting behavioral change and mental health, the legal, ethical, and societal implications remains largely unexplored. This article presents a qualitative case study of S.A.R.A.H. (Smart AI Resource Assistant for Health), a genAI chatbot developed by the World Health Organization (WHO), analyzed against the six ethical principles outlined in the WHO's 2021 Guidance on Ethics and Governance of AI for Health. We also gathered exploratory insights from adolescent focus groups. These findings are descriptive and not based on formal thematic analysis. Drawing on this analysis, we identify key gaps between high-level ethical principles and practice and offer policy recommendations to guide responsible use of AI chatbots for health promotion. ... Read more