Beyond witnessing: ethical imperatives in action-research on suffering, victims of violence, and structural harm in mental health systems

Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.

Patient perception of the use of artificial intelligence in dentistry: a systematic review and single-arm meta-analysis

Navigating ethical approval challenges: A case of cross-national multi-method social science project

Advances and limitations in angiogenesis assays: Integrating in vitro, in vivo, and emerging technologies.

It is estimated that, globally, the mean point prevalence of diagnosable mental disorders in children and adolescents is higher than 11%, and around half of cases of major mental disorders have their onset before the age of 18. Mental disorders with onset in childhood or adolescence have an enormous impact on the developing brain, body and personal identity, as well as on the short- and long-term social, educational and functional capacity of individuals. Child and adolescent psychiatry - as a discipline, profession, academic field, and network of clinical services - is still relatively young, with its formal evolution beginning in the 20th century. Therefore, it is not surprising that there are currently many challenges, but also opportunities and expected future developments, in this area. In this paper, we identify and address the core challenges, possible solutions, opportunities, and future directions of child and adolescent psychiatry. In the first part of the paper, challenges and possible solutions are discussed regarding diagnostic issues, stigma, access to care, shortage of mental health professionals, evidence-based treatments, treatment adherence, parental participation/engagement, integration with schools, digital influences and cyberbullying, and war/forced displacement. In the second part, opportunities and developments are addressed that relate to early identification and intervention, resilience, interdisciplinary collaborations, integration with primary care, community-based approaches, use of digital technologies, precision child and adolescent psychiatry, artificial intelligence and related ethical issues, and cultural diversity and competences. Despite the significance and impact of mental disorders in children and adolescents, clinical delivery and research on these conditions remain underfunded and underprioritized, even in high-income countries, with clinical services and prevention/early intervention research receiving minimal investment. Addressing mental health in children and young people requires multi-level strategies beyond individual treatment, including tackling structural and socioeconomic barriers and creating opportunities for strengthening resilience and well-being. A well-trained workforce, adequate policies, and increased public awareness are crucial. Overall, the current gaps demand urgent action and global funding rebalancing to more adequately meet the critical needs of children and young people challenged by mental illness.

The ethical dimensions of big data in refugee contexts: A scoping review of empirical studies in the social sciences

Despite drastic advances in psychiatric genetics, comparatively little attention has focused on the translation of those discoveries into real-world impact. This paper reviews the processes and considerations for integrating new techniques into clinical practice and provides an overview of areas of medicine where polygenic scores (PGS) are already being incorporated. We evaluate current PGS across three areas of psychiatry (depression, substance use disorders, and schizophrenia) against the criteria used by the National Electronic Medical Records and Genomics consortium to select PGS to study in a clinical context, finding that the PGS for psychiatric conditions are comparable to the PGS being implemented in clinical practice for other medical conditions. We conclude by discussing next steps for evaluating psychiatric PGS for clinical implementation including ethical issues that must be considered, and the need for more research on clinical utility to evaluate whether and how PGS can be used to improve behavioral health outcomes.

The smart city is a shifting promise defined by current objectives. This means that smart cities redefine themselves as issues such as connectedness, sustainability, or equality become politically, commercially, and or socially more or less appealing. Certain seemingly invisible factors can impact the extent to which the promise of smart governance and infrastructure come to fruition. This paper examines Amsterdam's internal “smart” soft infrastructure, from politics, to policies, to practice. This overview, stemming from interviews and reports, uncovers how policies, funding, and government structure determine what the smart city is and the extent to which it can be inclusive and accessible for all. The Amsterdam case reveals that similar smart cities have the potential to suffer from short term projects, ethical issues unextractable from data dependency, and conflicting needs of diverse city-dwellers. These concerns make it such that independent self-funded forces with long-term visions may wield comparative power in smart urban development. • Qualitative analysis of Amsterdam reveals soft infrastructure roadblocks. • Political shifts drive Amsterdam to reject the specific smart city label. • Short-term pilot projects hinder sustainable, long-term urban inclusion. • Diverse citizen needs create conflicts in smart infrastructure design.

This study examined ethical challenges in patient-provider communication within Ghana's healthcare system, with particular attention to confidentiality, provider rudeness, and truth-telling dilemmas in clinical disclosure. The study was conducted in two public hospitals in northern Ghana that serve diverse patient populations and operate within resource-constrained healthcare environments. Fifteen participants were purposively recruited, comprising eight healthcare providers and seven patients drawn from Tamale Central and Tamale West Hospitals. This study employed a qualitative research design using semi-structured interviews and theoretically informed thematic analysis. The analysis was guided by deontological and consequentialist ethical frameworks to interpret recurring ethical issues in patient-provider communication. Three interrelated themes emerged from the data. First, participants described breaches and tensions surrounding confidentiality and the management of sensitive patient information. Second, patients reported experiences of provider rudeness, dismissive conduct, and poor interpersonal engagement, which undermined trust and discouraged open communication. Third, the study identified truth-telling dilemmas in clinical disclosure, particularly where serious diagnoses, emotional distress, and family involvement complicated decisions about when, how, and to whom health information should be communicated. Across these themes, ethical communication was shown to be shaped not only by professional values but also by institutional pressures, hierarchical role boundaries, and the relational demands of care. Ethical communication is central to patient-centred care and to the moral legitimacy of healthcare delivery. The findings show that confidentiality, respectful interpersonal conduct, and ethically sensitive disclosure practices are critical to sustaining trust, protecting patient dignity, and improving care outcomes. The study recommends strengthening communication ethics training, clarifying disclosure guidelines for sensitive health information, and embedding accountability mechanisms that support ethically grounded patient-provider interaction in Ghana's healthcare system.

Older adults' perceptions and previous expectations about retirement and its causes: a qualitative study.

This paper presents results from a key informant survey exploring ethical, legal, and social issues (ELSI) as perceived by professionals working in the U.S.-based private sector human genomics industry. Drawing on a structured survey of 111 participants-including researchers, executives, policy leads, and communications specialists-this study examines how industry insiders assess emerging ethical concerns across domains such as data privacy, race and ancestry, sociogenomics, commercialization, and government partnerships. The survey instrument was developed based on a prior interview phase and reflects concerns raised directly by professionals in the field. Quantitative responses were analysed for levels of concern and consensus, while open-text responses provided further insight into areas of divergence and organizational context. Respondents expressed high concern and consensus about lack of diversity in datasets, fragmented privacy regulation, and potential genomics collaborations with governments implicated in human rights abuses, though significant disagreement emerged around the appropriate use of race categories and socio-genomic applications. This preliminary exploration offers a glimpse into perspectives from a difficult sample population, and suggests entry points for future collaboration, policy, and critical scholarship.

ABSTRACT This study examined Japanese high school students’ perceptions of the human elements of science (HEOS) within the nature of science (NOS) framework, an area for which limited empirical evidence is currently available. Participants included 605 students from three public high schools in southern Japan. Data were collected using an online questionnaire, and non-parametric tests were conducted to examine differences by gender, course type, and grade level. Overall, students expressed neutral views on the suitability of boys/men and girls/women for science education and did not strongly endorse gender-based differences. Female students in both course types believed that girls/women are not suitable for engineering. Students valued socio-emotional competencies, imagination, and creativity, alongside cognitive competencies, for scientists, but tended to underestimate the social aspects of scientific practice. Most students thought that scientific questions could be answered through observations and experimentation; however, some expressed doubts. They agreed that the development of science and technology benefits society and vice versa. Although they acknowledged science’s negative influence on society such as ethical issues caused by the development of genetic modification, they believed that scientific assistance could address these issues. Further research is needed to clarify how students can learn HEOS and related social values.

ABSTRACT The rapid advancement of artificial intelligence (AI) and computational intelligence (CI) technologies has significantly transformed the gaming industry, affecting both gamers’ perceptions and video game development processes. This article provides a comprehensive review of the applications of these technologies within the video game industry, examining their diverse uses, benefits, and associated challenges. By incorporating AI and computational intelligence (CI) in gaming, developers can make immersive and lifelike virtual environments. They use AI algorithms to create realistic images and mimic complex physics. These tools also improve the behavior of non-player characters (NPCs). By using machine learning and neural networks, NPCs can show adaptable and intelligent behaviors. This makes the game more demanding and fun for players. AI-powered procedural content generation methods can automatically make huge game levels, maps, and quests. This approach cuts down on production time and makes games more varied and enjoyable to play again. These technologies are not only helpful in content creation but also for creating user experience models, altering the level of complexity of the game dynamically, and implementing robust anti-cheating systems. The prevalent usage of these technologies, however, raises concerns about debugging, ethical issues such as privacy and biases within algorithms, and the computational power required by advanced AI models. The current research explores these issues, concluding with a discussion about future trends and the immense capabilities of AI and CI technologies to shape the future of interactive gaming.

This study examines the ethical issues related to AI-driven supply chain optimization, such as algorithmic biases, the effects of automation on employment, and accountability and transparency. Given the goal of increasing efficiency, machine learning, predictive analytics, natural language processing, and artificial intelligence (AI) are being actively used in a variety of industries, including retail, healthcare, and logistics. Also, technologies are automating and improving tasks such as inventory tracking and demand forecasting. This lowers cost and increases supply chain flexibility. However, using them raises significant ethical problems, specifically the issue of making fair choices. With the presence of bias in the trained systems, there will be unfair distribution of resources and the conditions that define the consequences of decisions, such as the introduction of high-value goods over fundamental needs, and in this case, the population needs it most. Another important issue is job loss, especially in low-skilled jobs, as automation becomes the norm in the logistics industry. The study suggests that AI systems should adopt ethical principles, such as fairness, transparency, and accountability. It suggests practical steps that businesses should take to employ AI in ways that ensure everyone gets fair results. The study continues by emphasizing the need to be aware of ethical issues to use AI to improve efficiency while also promoting fairness and sustainability in global supply chain management.

Artificial intelligence (AI) technology is developing rapidly in the medical field, particularly showing significant clinical value in the diagnosis and management of diseases in preterm infants. Preterm infants have immature organ development and a high incidence of complications; early prediction, accurate diagnosis, and individualized treatment pose major clinical challenges. With its powerful data processing and pattern recognition capabilities, AI provides new solutions for the diagnosis and management of diseases in preterm infants. It is now widely applied to the prediction of complications, imaging diagnosis, optimization of treatment plans, and prognostic evaluation for preterm infants, significantly improving diagnostic and therapeutic efficiency and accuracy. However, limitations remain in the clinical application, including data quality, model interpretability, and ethical issues. This article reviews the research progress of AI in the diagnosis and management of diseases in preterm infants, discusses its application advantages, challenges, and future directions, aiming to provide a reference for clinical practice and related research.

The use of human genetic variation to comprehend complex diseases has also introduced several ethical, legal, and societal issues (ELSI). Despite the important contribution of Human Genome Research (HGR), full comprehension of these issues by all its stakeholders remains challenging. This research focused on exploring the understanding of research participants on the informed consent process of HGR. Nineteen Ethiopian TB patients, who participated in genomics research, were purposely selected for in-depth interviews. Their recorded responses were transcribed into text-based versions and analyzed thematically using MAXQDA Analysis Software. The patterns in the informed consent process showed the following themes: Therapeutic misconception, Limited clarity on perceived benefit, Partial understanding of third-party use, Addressed suspicion or concerns, Mixed feelings about sample and data extraction, Concerns about sample governance, Variations in information and understanding, Barriers and facilitators to comprehension, and Volunteerism and influencing factors. The findings incorporated both the strengths and challenges of the consenting process of a genomic study, while informing the need for ethical improvements.

Artificial intelligence (AI) is considered to be a leading technology in radiation medical physics, which has the potential for improving efficiency and precision in imaging, radiotherapy, and nuclear medicine. Nonetheless, its application in the clinical setting is hindered by education, regulation, and ethical issues. To evaluate the views of medical physicists on AI adoption, potential issues, perceived advantages, ethical issues, and training needs. A cross-sectional survey in the form of an online questionnaire was distributed internationally to practicing medical physicists, representing specialties in radiation therapy, diagnostic imaging, and nuclear medicine. The instrument was used to capture demographic variables and AI familiarity, perceived benefits and barriers, ethical issues, training preferences, and future expectations. Descriptive statistics, chi-square tests, and binary logistic regression were performed. Open-ended responses were analyzed using thematic analysis. Most respondents were moderately familiar with AI, but nearly half had not incorporated AI into their practice despite being interested. Adoption patterns differed significantly across geographic regions, with higher usage reported in developed countries. AI familiarity was strongly associated with adoption. The most prominent benefits noted were dose calculation accuracy, patient safety, and workflow efficiency. In contrast, the main obstacles were inadequate training, high implementation costs, absence of standardized protocols, and low availability of advanced equipment. Ethical apprehensions focused on accountability and reduced human oversight. Preferred educational strategies included hands-on workshops and on-the-job training. Regional analysis and modeling revealed variability in AI adoption and identified key predictors, including AI familiarity and specialization. Results indicate that AI is considered an important technology in radiation medical physics. However, implementation was influenced by regional and professional differences. These findings highlight the necessity to expand AI education, create standard training programs, improve infrastructure, and provide clearer governance frameworks to support safe and responsible AI integration.

The transition away from animal experimentation is gaining momentum across scientific disciplines, driven by technological advancements, ethical imperatives and policy support. At the forefront of this movement is the development of New Approach Methodologies (NAMs). Rather than serving as mere substitutes for animal testing, NAMs represent a paradigm shift. Their growing acceptance has been reinforced by recent policy advancements, such as the US Food and Drug Administration Modernization Act 2.0 and the European Chemicals Agency's commitment to transitioning toward an animal-free regulatory system, both of which promote the adoption of NAMs. However, the term NAMs has become increasingly ambiguous, leading to confusion and potential misuse. To address this, we propose a unified definition: NAMs are species-specific methodologies, not including the use of living animals. NAMs prioritize the use of the target species in some form or another, eliminating the need for interspecies extrapolation. If the target species is human, this simple definition implies that working with experimental animals as a model never qualifies as a NAM. This distinction positions NAMs as a fundamentally different approach to the 3Rs framework of Replacement, Reduction and Refinement, and practical examples are discussed. Critically, this definition extends beyond toxicology and regulatory science, encompassing basic, applied and translational research. By leveraging cutting-edge technologies, including organoids, organ-on-chip, microfluidics, 3D bioprinting, bioinformatics, artificial intelligence and machine learning, NAMs provide novel biological insights that differ from those obtained through animal models while enhancing species-relevant data accuracy. Although challenges remain, particularly in their validation and widespread adoption, continued innovation, policy support and interdisciplinary collaboration will be key to unlocking their full potential, fostering more sustainable, humane and human-relevant approaches in biomedical research.

ABSTRACT Widespread use of generative artificial intelligence (GenAI) tools, such as ChatGPT, has disrupted education, particularly in accounting, where ChatGPT can perform well in traditional assessments. This study examines ChatGPT's impact on accounting education across five countries, exploring educators’ perceptions, challenges, adaptations, experiences and institutional responses. Despite calls to embrace GenAI, accounting educators expressed concern about the implications. This study provides a snapshot of their views, their insights, investigating challenges, experiences and innovative pedagogical strategies. A thematic analysis of reflections from accounting academics across the UK, Ireland, South Africa, Australia, and New Zealand, supplemented by focus groups, captures educators’ lived experiences. These included fears about academic integrity, seeking comfort in invigilated exams, uncertainty about ethical issues and varying institutional support. Many educators felt GenAI could facilitate opportunities to enhance learning, rethink assessment design, and should be embraced similarly to previous technological disruptions to accounting education. This study contributes to accounting education by documenting how accounting educators across five countries responded to the emergence of ChatGPT. It captures the strategies that educators described using to protect assessment integrity and support student learning in the early stages of GenAI adoption, offering insight into how accounting educators navigated this period of disruption.