Search Results (3,054)

Perinatal mental health remains a critical global challenge, with maternal mortality, preterm birth, and persistent disparities in care contributing to adverse outcomes for mothers. In addition, mental health difficulties in the perinatal period are associated with poorer developmental outcomes for young children and impose an economic burden on societies. Addressing these issues requires innovative approaches that can complement traditional clinical practices. Artificial intelligence (AI) has emerged as a powerful tool with the potential to transform perinatal care by enabling early risk prediction, personalised interventions, and scalable support systems. However, there are no existing reviews on use of AI across different stages of perinatal mental health. We conclude with a call to action for clinicians, researchers, policymakers, and technology developers to collaborate on a consensus framework that ensures ethical, safe, and equitable integration of AI into perinatal care. Full article

The persistent shortage of donor organs and the inherent drawbacks of autologous grafts highlight the urgent need for advanced biomaterial scaffolds in regenerative medicine. Synthetic polymers and animal-derived matrices offer structural support, yet they frequently lack the biological complexity of native tissue or carry translational liabilities—xenogeneic antigens, pathogen transmission risk, and batch variability. This review positions the human placenta as a compelling, ethically sourced, and abundant reservoir for fully human, xeno-free biomaterials. We examine the placenta’s distinct anatomical compartments and their rich complement of extracellular matrix (ECM) proteins, growth factors, and bioactive cytokines. These components confer potent pro-angiogenic, anti-inflammatory, antimicrobial, and immunomodulatory properties, enabling precise direction of cellular behavior and tissue regeneration. We systematically assess recent advances in decellularization and the processing strategies required to preserve these bioactivities while eliminating immunogenic material. By integrating current tissue engineering applications with the regulatory and ethical frameworks shaping clinical translation, we argue that placenta-derived matrices are uniquely positioned to transcend the limitations of conventional scaffolds and serve as a robust platform for future regenerative therapies. Full article

Background: Obesity is a chronic, multifactorial disease that demands personalized and sustainable management approaches. Digital health technologies, such as artificial intelligence, wearable devices, mobile health apps, and virtual reality (VR), may support obesity care by providing enhanced behavioral monitoring, personalized feedback, and patient engagement. Objective: This critical narrative review discusses the current evidence on artificial intelligence, wearable technologies, and VR in the context of precision nutrition and obesity management and their possible clinical applications and limitations. Method: A critical narrative review was conducted using peer-reviewed literature published between 2019 and 2026 and identified through PubMed and Google Scholar. Search terms included combinations of “precision nutrition,” “personalized nutrition,” “obesity,” “weight management,” “metabolic health,” “digital health,” “artificial intelligence,” “machine learning,” “mobile health,” “wearable devices,” and “omics” using Boolean operators. Evidence from randomized controlled trials, systematic reviews, meta-analyses, and key conceptual studies was critically synthesized due to substantial heterogeneity in interventions and outcomes. Result: Wearables and mobile applications can enable continuous self-monitoring of physical activity, dietary intake, sleep, and physiological measures. Artificial intelligence may improve dietary personalization, risk prediction, glycemic control, and adaptive feedback. VR offers an immersive way to tackle behavioral and cognitive mechanisms related to overeating such as cravings, food cue reactivity, and inhibitory control. However, the evidence is heterogeneous, with many studies limited by short follow-up periods, small samples, variable adherence, and insufficient clinical validation. Conclusions: Artificial intelligence, wearable technologies, and VR are promising tools for precision obesity management, but their long-term clinical effectiveness remains uncertain. Future research should prioritize adequately powered trials, longer follow-up, standardized outcomes, transparent algorithms, ethical data governance, and integration with multidisciplinary nutrition and obesity care. Full article

Advances in machine learning, predictive analytics, and clinical prediction modeling have accelerated the development of algorithmic tools for estimating reproductive outcomes after cancer treatment. In female oncofertility counseling, these models may support individualized assessment of treatment-related amenorrhea, premature ovarian insufficiency, and fertility risk, thereby improving risk communication and timely fertility-preservation referral. However, their use raises ethical concerns beyond predictive accuracy. This narrative review examines algorithmic prognostication in female oncofertility counseling, focusing on predictive uncertainty, surrogate reproductive endpoints, missing data, heterogeneous datasets, limited external validation, algorithmic bias, reproductive inequity, and the influence of algorithmic authority on patient autonomy and shared decision-making. We argue that predictive algorithms should be understood as decision-support tools rather than determinants of reproductive futures. Responsible implementation requires transparency, explainability, fairness assessment, ongoing validation, and meaningful human oversight. Algorithmic risk estimates should be communicated as conditional and contextual probabilities within patient-centered counseling, ensuring that predictive tools support informed, transparent, and value-concordant fertility-preservation decisions for women facing cancer treatment. Full article

Introduction: Artificial intelligence (AI) is becoming part of everyday knowledge work through generative AI, decision-support systems, algorithmic management, and AI-enabled organizational information systems. This development raises a central question: when does AI support employees, and when does it become a source of technostress, surveillance, uncertainty, and reduced autonomy? Objectives: This PRISMA-ScR scoping review mapped evidence on human-centered AI adoption in knowledge-intensive work and examined links with technostress, trust, autonomy, and employee well-being. Methods: Using a population–concept–context approach, we included peer-reviewed journal articles and conference papers addressing AI adoption in knowledge-work settings and at least one human-centered or employee-related outcome. Publicly accessible databases and public metadata records were searched across Scopus, Web of Science Core Collection, IEEE Xplore, ACM Digital Library, ScienceDirect, PubMed/MEDLINE, Business Source Complete, and APA PsycINFO. Searches were conducted in March 2026 and verified between 1 and 15 April 2026. Results: Twenty-six sources were included, comprising 17 journal articles and 9 peer-reviewed conference/proceedings sources. Five evidence clusters were identified: AI as a resource-demand system, information-system properties, generative AI work redesign, organizational implementation conditions, and short- versus long-term employee outcomes. Conclusions: Human-centered AI adoption in knowledge work requires transparent system design, organizational governance, employee participation, and long-term monitoring. The review contributes a business informatics implementation framework for trustworthy, ethically governed, and well-being-oriented AI use. Full article

Nanotechnology is rapidly transforming the perspective on pediatric neurology, enabling diagnostic, therapeutic, and monitoring strategies tailored to the unique features of neurological illnesses in children. This review acknowledges the problems caused by delays in diagnosis, the limitations of conventional procedures, and the need for new, focused approaches, highlighting recent advances in nanoscale materials and smart nanocarriers. Specifically, this paper summarizes advances in nanomaterials that can overcome physiological barriers, such as the developing blood–brain barrier (BBB) and age-dependent pharmacokinetics. We discuss innovations in stimuli-responsive delivery systems, theranostic platforms, and multimodal nanohybrids designed for precise targeting and real-time treatment monitoring. Special emphasis is placed on pediatric-specific considerations, including developmental differences in immune and metabolic responses, the necessity for age-adjusted dosing, and the potential long-term safety implications of nanoparticle exposure. Transformative applications are explored in various pediatric neurological conditions, including brain tumors, epilepsy, neurodevelopmental disorders, and rare degenerative diseases, emphasizing both achievements and challenges in translation. This paper evaluates various regulatory, ethical, and societal factors, alongside the integration of converging technologies such as AI-driven nanoparticle optimization, brain organoids, and digital twins to accelerate personalized therapy development. Conclusively, this paper emphasizes the importance of interdisciplinary collaboration, pediatric-focused clinical trial designs, and sustained investment to fully realize the potential of nanotechnology in improving neurological outcomes for children. Full article

The interest of agricultural economists in questions of animal welfare has been steadily growing. This conceptual review examines whether the methodological approaches used in this literature are consistent with the normative frameworks established by animal ethicists. We find that descriptive work—including preference elicitation among consumers and farmers and institutional approaches to implementing better conditions for farmed animals—is broadly compatible with animal ethics. However, the literature that assigns monetary values to the lives of non-human animals is more difficult to align with animal ethics principles. Comparing the human life valuation literature with nascent approaches to animal life valuation, we show that methodological differences can be identified as speciesist, and that assigning monetary values to animal lives is likely to remain fundamentally anthropocentric, because money itself is an exclusively human social institution. Full article

Population aging and the rising prevalence of motor disorders are driving demand for assistive lower-limb robotic systems capable of decoding user intention rather than merely providing mechanical support. We present CM-FuseNet, an attention-augmented hybrid Brain–Computer–Muscle Interface (BCMI) that simultaneously fuses cortical concentration indices extracted from electroencephalography (EEG) and lower-limb intention patterns derived from electromyography (EMG) to adaptively control a 4-DOF assistive lower-limb exoskeleton. To eliminate the burden of human-subject ethics review and to ensure reproducibility of the proposed methodology, all validation is performed exclusively on (i) permissively licensed open-access biomedical datasets, (ii) high-fidelity OpenSim 4.5 and MuJoCo 3.1 musculoskeletal–exoskeleton co-simulation, and (iii) limited self-experimentation by the corresponding author with non-invasive consumer-grade devices. Three components are introduced: (i) a log-tanh normalized concentration index CI in (0, 1) derived from the $(P_{SMR}+P_{Mid-Beta})/P_{Theta}$ ratio; (ii) a bidirectional Cross-Modal Transformer (CMT) with eight-head self- and cross-attention; and (iii) a Soft Actor-Critic (SAC) reinforcement-learning controller that adaptively tunes four servo PID gains using a concentration-weighted state. Experiments on the PhysioNet EEGMMIDB, Ninapro DB2/DB7, HuMoD and WAY-EEG-GAL datasets (combining N = 162 trial sessions, 47,520 windows, and five-fold cross-validation) yield a gait-phase classification accuracy of 96.84 ± 1.18%, torque-tracking RMSE of 0.072 ± 0.008 N·m, information transfer rate of 38.6 bits/min, end-to-end latency of 9.4 ms, and a 27.4% reduction in simulated metabolic cost over an EMG-only PID baseline (one-way ANOVA: F(4, 75) = 47.83, p < 0.001; Tukey HSD: p < 0.01 against all baselines). Under high cognitive load, CM-FuseNet preserves accuracy with only a 4.63 percentage-point degradation versus 13.22 percentage points for the EMG-only baseline. Full article

Language barriers hinder healthcare, particularly during case history-taking, a key part of diagnosis. While multilingual artificial intelligence (AI) chatbots offer solutions, there is fragmented evidence of their effectiveness and impact. This systematic review followed PRISMA 2020 guidelines, examining studies published between 2015 and 2025 on multilingual AI chatbots in healthcare across four databases (Google Scholar, Scopus, Web of Science, and PubMed), using a two-stage screening process. Data extraction focused on applications, supported languages, underlying technologies, target populations, and clinical outcomes. From 503 records, 49 studies, covering primary care, telemedicine, oncology, mental health, and other areas, met the criteria. Supported languages included English, Spanish, Arabic, Chinese, Hindi, and other underrepresented languages. In individual system evaluations using heterogeneous methodologies and evaluation settings, AI chatbots achieved a diagnostic accuracy ranging from 72–92%. Core technologies included large language models (LLMs), bidirectional encoder representations from transformers (BERT), a generative pre-trained transformer (GPT), retrieval-augmented generation (RAG), speech recognition, and distillation. The findings show that these improve clinical workflow (30–70% time savings) and patient engagement, reduce language barriers, and promote health equity. However, the overall evidence certainty was low to moderate, reflecting the predominance of prototype and proof-of-concept studies. Multilingual AI chatbots demonstrate a boost in healthcare efficiency, a reduction in language barriers, and the promotion of health equity, but exhibit challenges regarding validation, workflow integration, and evaluation standards, along with ethical issues such as privacy and bias. Future research should include real-world studies, diverse populations, standardized outcome measures, and long-term equity assessments. Full article

This review examines the environmental transition of contemporary diets, the effects of plant-based diets on human health, and vegetarianism as a phenomenon extending beyond a dietary choice, offering insights into its relationship with quality of life, physiological mechanisms, psychosocial determinants, and structural implications. In the current landscape, growing interest in vegetarianism is driven by three main factors: its contribution to multiple domains of health, its substantial potential to reduce resource use and environmental impacts, and its association with ethical and animal-related considerations. However, further research is needed to consolidate its role as a key component of future development, requiring integrated educational, cultural, technological, and policy approaches that ensure equitable access to plant-based foods, foster critical reflection on animal welfare and moral responsibility, as well as support coordinated transformations of food systems toward healthier and more environmentally sustainable societies. Ultimately, the global implementation of dietary shifts toward plant-based patterns remains anticipated but constrained, as ingrained economic and cultural interests continue to delay this pivotal systemic change. Therefore, until policy authorities enact substantial structural reforms and individuals actively promote this transition within their immediate social environments, the issue is likely to persist as a subject of ongoing debate and resistance against its operationalization. Full article

Hematologic malignancies, including acute myeloid leukemia, myelodysplastic syndromes, lymphoma, and multiple myeloma, are characterized by profound biological heterogeneity and highly dynamic treatment trajectories that conventional, static prognostic systems incompletely capture. Cellular therapies, such as chimeric antigen receptor T-cell therapy and hematopoietic stem cell transplantation, offer potentially curative options for relapsed or refractory disease, yet outcomes remain highly variable, and management decisions regarding conditioning intensity, lymphodepletion, immunosuppression, and toxicity surveillance continue to be largely protocol-driven rather than individually adapted. Artificial intelligence (AI) and machine learning (ML) have demonstrated substantial promise in diagnostic support, prognostic stratification, and multimodal data integration across hematologic malignancies, but existing models remain predominantly static and related to pre-treatment in orientation, limiting their utility for real-time clinical guidance. This review summarizes current AI applications in hematologic oncology; critically compares the strengths, limitations, and clinical applicability of major AI model classes, including traditional machine learning, deep learning, multimodal integrative frameworks, reinforcement learning, digital twins, and emerging foundation models and large language models; and proposes an adaptive, multimodal paradigm. We examine key enabling technologies and address the clinical, regulatory, ethical, and implementation challenges that must be resolved before these systems can be deployed at the bedside. We argue that the central challenge facing the field is no longer whether AI can predict outcomes, but whether it can actively guide real-time therapeutic decisions, and that achieving this transition will require interdisciplinary collaboration, prospective validation, and governance frameworks capable of ensuring interpretability, equity, and clinical trustworthiness. Full article

This study presents a bibliometric analysis of AI healthcare research related to implementation science and governance frameworks from 2016 to 2026. A systematic search of the Scopus database identified 3780 peer-reviewed articles published across 1500 sources. The dataset was analyzed using Biblioshiny. The findings show an annual growth rate of 47.65% in governance-focused publications, exceeding the growth rate of technical AI research. Four main research themes were identified: regulatory compliance, ethical frameworks with limited operational measures, organizational readiness, and clinical workflow integration. The United States, China, and the United Kingdom are the leading contributors, while the Journal of Medical Internet Research and BMJ Open are among the main publication outlets. International collaboration (34.66%) remains concentrated among high-income countries. Thematic development has progressed from general ethical discussions to pandemic-related applications and more specific regulatory frameworks. Three research gaps contribute to the algorithm-to-policy translation deficit: the principles–practice gap, the regulatory–evidence gap, and the innovation–implementation gap. This study proposes an integrated governance framework based on five evidence-based principles. The framework is a conceptual model derived from bibliometric findings and requires further empirical validation in clinical settings before practical adoption. The study contributes by providing a bibliometric analysis of AI governance research and introducing the concept of the “algorithm-to-policy translation deficit” as an analytical framework. It also offers a structure to support future research and practice toward safe, effective, and equitable clinical implementation of AI. These findings guidance for regulators, healthcare organizations, AI developers, and researchers. Full article

Human–animal therapeutic relationships are commonly evaluated through their effects on human psychological, emotional and social well-being, whereas the experiences and welfare of non-human animals remain less consistently examined. This narrative interdisciplinary review analyses such relationships through interpretative asymmetry, integrating human–animal studies, ethology, animal welfare science and biological approaches to interspecies communication. Selected cultural examples are used only as illustrative boundary narratives. Interpretative asymmetry is defined not as a statistically measured variable, but as a conceptual and structural imbalance in interpretative authority, communicative access and agency. Humans define therapeutic goals, organize interaction and attribute meanings such as trust, reciprocity or support to non-human animal behaviour. Non-human animals respond through species-specific sensory, behavioural and physiological mechanisms that may indicate affiliation, habituation, stress regulation, inhibition or conflict avoidance. Animal-assisted interventions and animal-assisted therapy (AAI/AAT) should therefore be assessed through human benefit and animal welfare indicators, including workload, rest, agency, withdrawal opportunities and handler competence. Dogs and horses are discussed as applied contexts in which these asymmetries become visible, whereas wild-animal narratives are treated as boundary examples of projection and romanticization. Recognizing non-human animals as active, welfare-sensitive participants is essential for ethical therapeutic practice. Full article

Co-Creative Robotics combines computational creativity, robotic embodiment, and human–robot collaboration to support or generate creative behavior in physical and social environments. As these systems become more autonomous, data-intensive, and interactive, cybersecurity and privacy can no longer be treated as external safeguards added after creative functionality has been designed. This PRISMA-informed review investigates whether principles of cybersecurity-by-design and privacy-by-design can be integrated into Co-Creative Robotics without constraining creativity, autonomy, and user agency. The database search covered ACM Digital Library, Google Scholar, IEEE Xplore, Scopus, SpringerLink, and Web of Science, and was complemented by two focused backward and forward snowballing iterations. From 623 database records, the final synthesis includes 27 primary studies. The results show that direct literature combining cybersecurity, privacy, and Co-Creative Robotics remains limited, but evidence from creative HRI, social-robot privacy, cyber-physical security, privacy-preserving interaction design, security modeling, and robot ethics supports a conditional answer. Integration is feasible when security and privacy mechanisms are adaptive, explainable, participatory, context-sensitive, and lifecycle-aware. However, the evidence on transparency-oriented privacy mechanisms is mixed: improvements in awareness or acceptance do not consistently translate into reduced disclosure or greater perceived safety. Rigid controls may constrain creative exploration, whereas well-designed controls can support trust, accountable autonomy, safe embodiment, privacy-aware interaction, provenance, and agency-preserving creativity. The review proposes a conceptual lifecycle-oriented research agenda for secure and privacy-aware Co-Creative Robotics. Full article

Behavioural nudges modify choice architecture to steer food choices without removing alternatives or substantially changing economic incentives. This critical narrative review synthesises 62 sources from behavioural public policy, public health nutrition, food ethics, and sustainable food-system governance. It distinguishes healthy food nudges, sustainable food nudges, green nudges, and responsible food-system interventions, and compares availability, placement, defaults, labels, digital architecture, and mixed policy packages. The evidence shows that nudges influence immediate food selection, but effects are heterogeneous and weaker for sustained environmental outcomes. Price instruments address affordability, nudges address attention and convenience, and boosts strengthen decision competence; none can substitute for the others. Ethical acceptability requires a transparent, calibrated opt-out process, equity monitoring, disclosure of commercial motives, democratic accountability, and integration with structural policy. Public institutions and profit-driven digital platforms therefore require different governance safeguards. Nudges are most defensible as components of coordinated food-system policy rather than stand-alone solutions. Full article