Search Results (1,481)

Deep learning (DL) has transformed cancer imaging by enabling automated tumour detection, classification, and risk prediction. Despite impressive diagnostic performance, limited explainability and poor uncertainty calibration continue to restrict clinical integration. This review is guided by five research questions that examine the challenges, impact, and translational implications of explainable artificial intelligence (XAI) in oncology imaging. We identify key barriers to trust, including dataset bias, shortcut learning, opacity of convolutional neural networks, and workflow misalignment. Evidence suggests that explainable models can increase clinician confidence, reduce false positives, and improve collaborative decision-making when explanations are faithful, semantically meaningful, and uncertainty aware. We evaluate architectural strategies that embed interpretability such as concept-bottleneck models, prototype-based learning, and attention regularization along with post hoc techniques. Beyond performance metrics, we examine how interpretable AI aligns with clinical reasoning processes and analyse regulatory, ethical, and medico-legal considerations influencing deployment. The findings indicate that explainability alone is insufficient, durable trust requires epistemic alignment, prospective validation, lifecycle governance, and equity-focused evaluation. By reframing explainability as a structural design principle rather than a supplementary feature, this review outlines a pathway toward accountable and clinically dependable AI systems in oncology. Full article

The skin–brain axis constitutes a complex, bidirectional network integrating cutaneous sensory, immune, and neuroendocrine systems with central neural circuits involved in emotion regulation, stress responsivity, and social cognition. Advances in psychodermatology and cosmetic science have progressively extended this framework to the emerging field of neurocosmetics, which explores how topical formulations, sensorial properties, and cutaneous neuromodulators may influence psychological well-being, affective states, and perceived stress. The aim of this narrative review is to synthesize current evidence on the biological foundations of the skin–brain axis and to critically examine the implications of these mechanisms for neurocosmetic interventions from a psychological and psychiatric perspective. It describes the biological substrates underlying skin–brain communication, including the cutaneous hypothalamic–pituitary–adrenal axis, neuropeptides, neurotrophins, transient receptor potential channels, and endocannabinoid signaling, and examines how these pathways are targeted by neurocosmetic interventions. Particular attention is devoted to neuroactive compounds, such as peptides, cannabinoids, botanicals, and aromatherapeutic molecules, as well as to sensorial strategies involving texture, temperature, and olfactory cues, which may modulate mood, anxiety, and self-perception through peripheral mechanisms. From a psychological and psychiatric perspective, the review discusses the intersection between stress-related skin conditions, body image disturbances, and emotional dysregulation, highlighting how cosmetic practices may influence subjective well-being beyond purely aesthetic outcomes. Methodological limitations of the existing literature, including the heterogeneity of study designs and outcome measures, as well as ethical considerations related to mood- and stress-related claims in cosmetic products, are critically examined. Finally, future research directions are outlined, and a translational framework is proposed to integrate dermatology, neuroscience, and mental health within next-generation cosmetic science. Full article

Digital nudges refer to subtle modifications in digital choice architectures that are increasingly applied across domains such as healthcare, human–computer interactions, and behavioral science. However, existing approaches often overlook users’ needs, contextual factors, and ethical considerations related to transparency and autonomy. This systematic literature review, guided by PRISMA 2020, examines the integration of co-design methodologies in digital nudging across four dimensions: participants, application domains, nudge forms, and development methods. The findings show that co-design is primarily driven by end-users, supported by domain experts and technology specialists. Applications are concentrated in health-related contexts, particularly chronic disease management and mental health. The effectiveness of priming varied across studies, with some reporting short-term benefits and others indicating user fatigue, suggesting context-dependent impact and limited long-term effectiveness. Full article

Background/Objectives: Genetic identification of human skeletal remains plays a pivotal role in forensic investigations when other traditional or primary methods are not appropriate. Decomposition, storage and environmental conditions often leave the skeletal structure as the only basis for identification. This review synthesizes current methodologies and technological advances in damaged DNA extraction and analysis, emphasizing the forensic relevance of skeletal remains for genetic identification. Methods: A comprehensive literature analysis highlights the basis of genetic identification; sampling that considers intrinsic and extrinsic factors influencing the DNA yield and its quality; pre-treatment methods; extraction protocols that are suitable for its sensitivity; genetic marker panels that allow for human identification; and statistical evaluation and analysis of the results. The last chapter demonstrates the real-world impact of genetic identification on historical cases, underscoring its broader significance in legal, humanitarian, and socio-historical contexts, supporting a critical evaluation of best practices, methodological robustness, and ethical considerations within the field. Results: Teeth, femur and the petrous portion of temporal bone are the main samples used for genetic analysis. STR profiling and mitochondrial DNA are the gold standard markers for skeletal human identification. Minimally destructive protocols that enhance a high DNA yield are chosen, with silica-based methods being highlighted in the extraction protocols. Next-Generation Sequencing techniques have also improved analytical outcomes, by enabling high-throughput data generation, increased coverage depth, nucleotide-level sequence data, and high-level multiplexing of genetic targets. Conclusions: This review provides a comprehensive framework for researchers and practitioners seeking to optimize genetic identification workflows in forensic sciences and bioarcheology. These methodological advances have significantly increased identification success rates, especially in cases involving degraded or limited skeletal remains. Reviews such as this one help us to identify methodological gaps, ethical concerns, and future research directions, thereby establishing best practices when working with highly degraded skeletal material, supporting more reliable, standardized, and legally defensible applications of genetic identification in forensic, archeological, and humanitarian contexts. Full article

Due to the recent rapid development of artificial intelligence (AI) and its expanding impact on the planet, green and sustainable AI research has increasingly gained attention. This systematic literature review searches main databases, including Scopus, Web of Science, and Google Scholar, using an organized methodological approach. Following a thorough screening process, 49 final studies published between 2016 and 2026 are selected from an initial identification of 325 original records. We identify and analyze four key categories of sustainable AI practices: (1) model-level algorithmic efficiency, (2) hardware- and system-level optimization, (3) lifecycle- and data-centric approaches, and (4) operational and policy-level sustainability. We also highlight and explain four dimensions at the intersection of AI and environmentally responsible behavior: AI for sustainable applications’ development in industries, ethical considerations and accountability in using AI, and opportunities enabled by generative AI. We then combine existing taxonomies, evaluation metrics, and challenges to identify areas for improvement and suggest future research directions. Based on our analysis, we emphasize the need for interdisciplinary cooperation to facilitate responsible AI innovation and match it with global sustainable development goals (SDGs). We also highlight the importance of developing adequate frameworks along with precisely defined and standardized metrics to assess the environmental impact of AI. This review aims to encourage more responsible and environmentally friendly AI practices by providing a structured framework for researchers, educators, and professionals engaged in sustainable AI. Full article

As artificial intelligence (AI) systems become increasingly autonomous, scalable, and embedded in critical digital infrastructure, AI safety has emerged as a significant consideration for cybersecurity, system reliability, and institutional trust. Advances in large language models and agentic systems expand the threat surface to include misalignment, large-scale misuse, opaque decision-making, and cross-border risk propagation, while existing debates remain fragmented across technical, ethical, and geopolitical domains. This paper conducts a structured comparative analysis of AI safety perspectives from ten influential thinkers, examining them across five dimensions and reframing their insights through a cybersecurity lens spanning national governance, industry standards, and firm-level design. Building on this synthesis, the study proposes a layered control architecture that organizes technical safeguards, governance mechanisms, and human oversight into a defense-in-depth structure. The framework is conceptual and theory-building, intended to clarify system-level security reasoning and support future empirical refinement across diverse institutional contexts. Full article

Aging is a complex physiological process influenced by various factors, including individuals’ mental attitude. This interaction between biological vulnerability and psychological resources characterizes the entire life course; however, in older age, it becomes particularly salient due to the higher prevalence of multimorbidity, frailty, functional decline, and existential transitions (e.g., retirement, bereavement, loss of social roles), which intensify the impact of mental outlook on adaptation and quality of survival. Optimism has gained growing attention in clinical practice as a psychological asset associated with better health. This has also encouraged the incorporation of optimism-enhancing strategies into geriatric care. However, encouraging optimism in older patients, although well intentioned, can create ethical tensions in clinical communication, decision-making, and care planning. Sensitivity should be paid to aspects such as education, cultural background and religion within interactions with older adult patients. Uncritical promotion of optimism can undermine autonomy, foster unrealistic expectations, or place emotional burdens on patients who may already feel vulnerable. The appeal of optimism should therefore be balanced with careful ethical consideration. Full article

Autism Spectrum Disorder (ASD) is a lifelong condition marked by challenges in social communication and repetitive behaviors. Current treatments, primarily behavioral therapies, often fail to address the core symptoms. Recent research has explored the potential of psychedelics, such as LSD, psilocybin, and MDMA, as a new therapeutic approach. While these substances primarily modulate the serotonin 5-HT_2A receptor, their therapeutic effects also involve interactions with other serotonergic, dopaminergic, and glutamatergic pathways, collectively promoting neuroplasticity—the brain’s ability to change and adapt. The specific receptors’ activation leads to structural and functional changes in the brain that can enhance social behavior and emotional regulation. Studies show that psychedelics may reduce symptoms of conditions like treatment-resistant depression and PTSD, highlighting their therapeutic potential. For ASD specifically, psychedelics may improve psychological flexibility, reduce distress, and enhance social interaction. While promising, the use of these substances requires careful consideration. Psychedelics can induce intense experiences and altered states of consciousness, necessitating strict monitoring and support during therapy. Ethical guidelines, including informed consent, are crucial, especially for vulnerable populations. In conclusion, psychedelics hold significant promise for treating ASD and other psychiatric disorders by promoting neuroplasticity and modulating complex signaling pathways. Continued research and clinical trials, conducted with strong ethical oversight, are essential to realizing their full therapeutic potential. Full article

Artificial intelligence has been considered as a transformative element capable of reshaping STEM education into equitable, resource-efficient, and scalable learning environments. However, realizing this potential requires striking a careful balance between technological innovation, pedagogical considerations, and ethical concerns. This study sought to examine the implementation of artificial intelligence (AI) tools by STEM university faculty members in Saudi Arabia to promote Sustainable Development Goal 4 (quality education). While doing so, the study attempted to explore how Saudi STEM university faculty members integrated AI tools in their instructional practices and analyze their perceptions towards these tools. To achieve these goals, the study employed an explanatory sequential mixed-methods design. In the first phase of data collection, a close-ended questionnaire was applied to a random sample of (324) STEM university faculty members. The second phase involved gathering qualitative data using a semi-structured interview administered to 12 purposively selected experts. Key quantitative findings revealed an overall AI integration at a medium level with a mean of (2.71) and standard deviation of (0.36) across three instructional practices, namely planning, implementation, and assessment. The highest integration level was in assessment (M = 2.93, medium) while the lowest was in planning (M = 2.61, medium). The results also revealed that the participants’ perceptions towards integrating AI tools were highly positive (M = 4.00, high), albeit with some concerns regarding the effect of excessive and unguided use of AI tools on students’ higher-order thinking skills, particularly the risk of AI functioning merely as an information delivery mechanism rather than serving its more pedagogically valuable role as a brainstorming scaffold. Furthermore, the study unveiled a number of barriers to integrating AI tools, including the weakness of digital infrastructure, lack of professional development, the limited credibility of AI-generated content, and ethical concerns related to academic integrity and copyrights. The research suggests the establishment of a sustainable digital environment by improving the infrastructure, providing specific training in accordance with the principles of sustainability, and implementing policies that promote equitable, transparent, and responsible integration of AI. These strategies can coordinate the growth of technology with the larger needs of the quality of education, inclusion, and sustainability of STEM education in the long term. Full article

Background: Sexual health research with migrant and refugee communities presents unique challenges, shaped by cultural sensitivities, stigma, and the under-representation of these populations in health research. However, lived experiences insights are essential for the development of appropriate and useful research and health initiatives. It is important to learn from researchers’ experiences to expand the representation of migrant and refugee community voices. Method: This paper draws on two qualitative studies conducted in South Australia: one exploring the sexual and reproductive health perspectives of refugee and migrant women, and the other of men. We reflect upon the methodological and ethical considerations in conducting research in this sensitive field and provide recommendations for future researchers and healthcare providers when working with migrant and refugee communities. Results: Both studies encountered difficulties in relation to participant recruitment, cross-cultural communication, and addressing taboos surrounding sexual health. At the same time, they highlighted opportunities for generating meaningful insights through culturally safe, gender-sensitive approaches and collaboration with community stakeholders. Conclusions: By synthesising experiences from both projects, we identify practical strategies for building trust, overcoming linguistic and cultural barriers, and creating supportive environments for discussing sensitive topics. These reflections offer guidance for researchers and clinicians aiming to advance culturally responsive sexual health research and strengthen healthcare provision for migrant and refugee populations. Full article

In this review, we aimed to condense data on mammalian models of experimental infection with species of the genus Sporothrix, the causal agent of sporotrichosis, using the PRISMA methodology to search in three electronic databases: PubMed, Lilacs, and Web of Science. We analyzed the mammals used and the criteria that determine the course of the infection, including inoculum size and route of inoculation, the host’s immune status, and the fungal species employed, as well as information on ethical principles and criteria for determining the pathogenicity/virulence of the fungal species used, and presented a scoring system to be used in experimental infection studies in animal models alongside clinical parameters to assess the humane endpoint and provide reliable results while respecting animal welfare. Our results demonstrated that most articles described mice as mammalian models for experimental sporotrichosis. Over half of the articles cited an intermediate inoculum, ranging from 10⁶ to 10⁷ cells/mL. Subcutaneous is the inoculation route described in 27.71% of the articles, followed by intraperitoneal and intravenous routes, with 25.30% and 21.08%, respectively. Seventy-nine point five-two percent of the studies used immunocompetent models, 9.04% used immunosuppressed animals, and 10.84% included both immunocompetent and immunosuppressed animals. We also observed that Sporothrix schenckii was the most widely used species, considering both the entire period (1900–2024: 77.11%) and the period after the description of new species (2008–2024: 56.47%). Animal welfare conditions were poorly detailed in all articles. Only four studies reported a humane endpoint to terminate the experiment, and one presented consideration of the 3Rs (Replace, Reduce, and Refine). A few articles mentioned the most significant criteria grouped to evaluate the pathogenicity/virulence of the fungal species studied. Full article

Large Language Models (LLMs) have become foundational to modern Artificial Intelligence (AI), enabling advanced reasoning, multimodal understanding, and scalable human-AI interaction across diverse domains. This survey provides a comprehensive review of major proprietary and open-source LLM families, including GPT, LLaMA 2, Gemini, Claude, DeepSeek, Falcon, and Qwen. It systematically examines architectural advancements such as transformer refinements, mixture-of-experts paradigms, attention optimization, long-context modeling, and multimodal integration. The paper further analyzes alignment and safety mechanisms, encompassing instruction tuning, reinforcement learning from human feedback, and constitutional frameworks, and discusses their implications for controllability, reliability, and responsible deployment. Comparative analysis of training strategies, data curation practices, efficiency optimizations, and application settings highlights key trade-offs among scalability, performance, interpretability, and ethical considerations. Beyond synthesis, the survey introduces a structured taxonomy and a feature-driven comparative study of over 50 reconstructed LLM architectures, complemented by an interactive visualization interface and an open-source implementation to support transparency and reproducibility. Finally, it outlines open challenges and future research directions related to transparency, computational cost, data governance, and societal impact, offering a unified reference for researchers and practitioners developing large-scale AI systems. Full article

The rapid expansion of edible insect production has focused primarily on rearing, processing efficiency, safety, and nutritional composition, while the slaughter of insects has received comparatively little scientific and ethical scrutiny. This narrative review examines insect slaughter as a critical control point linking bioethics, physiology, and ingredient quality. The review synthesizes evidence from neurobiology, food science, and processing studies to evaluate how commonly used slaughter methods interact with biological aspects of insects. Existing literature shows that slaughter techniques influence protein stability and hydrolysis, lipid oxidation, antioxidant retention, techno-functional properties such as emulsification and gelation, as well as sensory attributes and consumer acceptance. Available evidence suggests that methods designed to rapidly suppress metabolic activity may be associated with improved preservation of certain nutritional and functional parameters, although findings remain species- and context-dependent. The review further highlights major knowledge gaps, including the lack of species- and life-stage-specific welfare indicators and standardized assessment protocols. Overall, the findings support the need to reconceptualize insect slaughter as a strategic upstream decision rather than a neutral processing step. Integrating ethical considerations with nutritional, functional, and regulatory perspectives is essential for establishing science-based standards and ensuring the responsible development of edible insect-based food and feed systems. Full article

Anorexia nervosa is a clinically complex and ethically challenging psychiatric disorder. Clinicians are frequently confronted with ethical dilemmas arising from conflicts between core ethical principles in everyday clinical practice. Professional codes of ethics and legal frameworks often fail to provide a stable basis for resolving these dilemmas due to the fluctuating medical risk and the ego-syntonic nature of anorexia nervosa. Under conditions of heightened responsibility and medical risk, clinicians’ cognitive and emotional responses may be activated and may mediate ethical decision-making. Although such internal processes have been described in the literature, limited attention has been paid to their role in shaping ethical judgment in routine clinical care. The aim of this article is to conceptualize the decision-making processes that unfold in response to ethical dilemmas in the clinical context of anorexia nervosa and propose a sequential multi-level framework. A focused conceptual literature review was conducted to develop a reflective framework for clinical practice, drawing on selected studies in clinical ethics, healthcare law, anorexia nervosa care, and cognitive theory. Clinicians’ internal cognitive and emotional processes play a significant role in ethical decision-making in complex clinical contexts such as anorexia nervosa and should be explicitly recognized and brought into reflective awareness through supervision and reflective practice. Ethical decision-making is therefore conceptualized as a dynamic process linking clinical events, clinicians’ internal responses, ethical and legal considerations, and reflective clinical judgment. Incorporating structured reflection into clinical, educational, and supervisory settings may support more ethically informed and context-sensitive clinical judgment within multidisciplinary eating disorder services. Full article

Background and Objectives: Artificial intelligence (AI) has transitioned to an integral part of dermatology in only few years, yet perceptions of its use vary widely, reflecting diverse hopes, concerns, and perceived clinical utility. Materials and Methods: In this study, 300 dermatologists from 13 countries, representing a range of experience levels and AI usage statuses, were surveyed regarding the characteristics and applications of AI in dermatology. Results: Among respondents, 61.33% reported having used AI tools in clinical practice. Adoption of AI was observed across all age groups, countries, and experience levels. Analysis of the types of AI tools used revealed a strong reliance on general-purpose large language models (LLMs), with chatbots being the most frequently cited category, utilized by 58.15% of users. Younger clinicians demonstrated a significant preference for chatbots (p < 0.05). Country-specific patterns in AI adoption were also noted. The most highly rated expected benefit of AI in dermatology was improved diagnostic accuracy, while the primary concern centered on regulatory and ethical limitations, suggesting that the “AI revolution” in dermatology is currently constrained less by technical barriers and more by regulation considerations. Use of consent forms when AI use takes place was more frequently reported as mandatory by dermatologists who had never used AI, reflecting heightened caution among non-users (p = 0.03). Additionally, 75% of respondents agreed that formal training in AI is necessary, highlighting a significant gap in traditional medical education regarding emerging technologies. Full article