Search Results (748)

Caring for the lives and welfare of animals is central to veterinary and animal sanctuary work, yet the meaning remains a subject of complex debates. Different stakeholders negotiate what constitutes appropriate care, leading to conflicting demands and expectations from internal and external sources. This article is based on two qualitative studies: Study I explores the multifaceted aspects of death work in farm animal medicine, emphasising the practical, emotional and ethical challenges involved. Study II examines human–animal interaction in sanctuaries, which reveal tensions between instrumental and relational care in animal-centred work. Relational care represents a subjectifying approach with individual attention to animals, while instrumental care is a more objectifying perspective based on species representation. These demands can often be contradictory, complicating day-to-day decision making under pressure. To analyse these complexities, this study employs Clarke’s situational analysis (social worlds/arenas mapping), providing a means of comparing care work across different fields. This approach highlights how actor constellations, institutional settings, and structural constraints influence the negotiation of care. Addressing these issues provides a more nuanced understanding of the professional challenges of animal-centred care and the necessary skills to navigate its inherent contradictions. Full article

The integration of artificial intelligence and human decision-making within blockchain systems has raised complex ethical considerations, necessitating the development of comprehensive theoretical frameworks. This research develops a multi-paradigm ethical framework addressing the ethical dimensions of hybrid intelligence—the dynamic interplay between human judgment and artificial intelligence—in the governance of blockchain technology and cryptocurrency systems. Drawing upon complexity theory and institutional theory, this study employs a theory synthesis methodology to investigate inherent paradoxes within hybrid intelligence systems, including how transparency creates new opacities in AI decision-making, decentralization enables centralized control, and algorithmic efficiency undermines ethical sensitivity. Through PRISMA-compliant systematic literature analysis of 50 relevant publications and theoretical synthesis, this research demonstrates how blockchain technology fundamentally redefines hybrid intelligence by establishing novel forms of trust, accountability, and collective decision-making. The framework advances three testable propositions regarding emergent intelligence properties, adaptive capacity, and institutional legitimacy while providing practical governance principles and implementation methodologies for blockchain developers, regulators, and participants. This study contributes theoretically by bridging the fields of complex systems and institutional analysis, integrating complex adaptive systems with institutional legitimacy processes through a multi-paradigm integration methodology. It delivers an ethical framework that addresses accountability distribution in Decentralized Autonomous Organizations, quantifies ethical challenges across major platforms, and offers empirically validated guidelines for balancing algorithmic autonomy with human oversight in decentralized systems. Full article

As climate change consequences intensify, higher education institutions (HEIs) have an opportunity and responsibility to model sustainable operations. This study examines how embracing shared knowledge and inclusion in sustainability decision making facilitates green human resource management (GHRM) efforts to invigorate organizational environmental performance. The study examines the effects of shared and inclusive governance on campus sustainability via a regression model and the mediating role of employee participation via a structural equation modeling approach. The results show that shared governance and inclusive governance positively predict the commitment of HEIs to reducing greenhouse gas emissions, and campus engagement mediates these relationships, underscoring the importance of participation. These findings align with stakeholder theory in demonstrating that diverse voices in decision making can enhance commitment to organizational goals like sustainability. The findings also highlight the importance of shared and inclusive governance arrangements at college campuses not only for ethical reasons but also for achieving desired outcomes like carbon neutrality. For campus leaders striving to “green” their institutions, evaluating cross-departmental representation in governance structures and promoting inclusive cultures that make all students and staff feel welcome appear as important complements to GHRM practices. Full article

Background/Objectives: Extracting meaningful medical information from chest X-ray images and transcribing it into text is a complex task that requires a high level of expertise and directly affects clinical decision-making processes. Automatic reporting systems for this field in Turkish represent an important gap in scientific research, as they have not been sufficiently addressed in the existing literature. Methods: A deep learning-based approach called Model-SEY was developed with the aim of automatically generating Turkish medical reports from chest X-ray images. The Swin Transformer structure was used in the encoder part of the model to extract image features, while the text generation process was carried out using the cosmosGPT architecture, which was adapted specifically for the Turkish language. Results: With the permission of the ethics committee, a new dataset was created using image–report pairs obtained from Elazıg Fethi Sekin City Hospital and Indiana University Chest X-Ray dataset and experiments were conducted on this new dataset. In the tests conducted within the scope of the study, scores of 0.6412, 0.5335, 0.4395, 0.4395, 0.3716, and 0.2240 were obtained in BLEU-1, BLEU-2, BLEU-3, BLEU-4, and ROUGE word overlap evaluation metrics, respectively. Conclusions: Quantitative and qualitative analyses of medical reports autonomously generated by the proposed model have shown that they are meaningful and consistent. The proposed model is one of the first studies in the field of autonomous reporting using deep learning architectures specific to the Turkish language, representing an important step forward in this field. It will also reduce potential human errors during diagnosis by supporting doctors in their decision-making. Full article

The integration of advanced computer vision and artificial intelligence (AI) techniques into collaborative robotic systems holds the potential to revolutionize human–robot interaction, productivity, and safety. Despite substantial research activity, a systematic synthesis of how vision and AI are jointly enabling context-aware, adaptive cobot capabilities across perception, planning, and decision-making remains lacking (especially in recent years). Addressing this gap, our review unifies the latest advances in visual recognition, deep learning, and semantic mapping within a structured taxonomy tailored to collaborative robotics. We examine foundational technologies such as object detection, human pose estimation, and environmental modeling, as well as emerging trends including multimodal sensor fusion, explainable AI, and ethically guided autonomy. Unlike prior surveys that focus narrowly on either vision or AI, this review uniquely analyzes their integrated use for real-world human–robot collaboration. Highlighting industrial and service applications, we distill the best practices, identify critical challenges, and present key performance metrics to guide future research. We conclude by proposing strategic directions—from scalable training methods to interoperability standards—to foster safe, robust, and proactive human–robot partnerships in the years ahead. Full article

The rapid proliferation of wearable sensors and advanced tracking technologies has revolutionized data collection in elite sports, enabling continuous monitoring of athletes’ physiological and biomechanical states. This study proposes a comprehensive big data analytics framework that integrates data acquisition, processing, analytics, and decision support, demonstrated through synthetic datasets in football, basketball, and athletics case scenarios, modeled to represent typical data patterns and decision-making workflows observed in elite sport environments. Analytical methods, including gradient boosting classifiers, logistic regression, and multilayer perceptron models, were employed to predict injury risk, optimize in-game tactical decisions, and personalize sprint mechanics training. Key results include a 12% reduction in hamstring injury rates in football, a 16% improvement in clutch decision-making accuracy in basketball, and an 8% decrease in 100 m sprint times among athletes. The framework’s visualization tools and alert systems supported actionable insights for coaches and medical staff. Challenges such as data quality, privacy compliance, and model interpretability are addressed, with future research focusing on edge computing, federated learning, and augmented reality integration for enhanced real-time feedback. This study demonstrates the potential of integrated big data analytics to transform sports performance optimization, offering a reproducible and ethically sound platform for advancing personalized, data-driven athlete management. Full article

The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice. Full article

Objective: There is growing interest in analyzing whether ethical climates influence the emotional states of organizational members. For this reason, the main objective of this study is to evaluate the relationship between a benevolent ethical climate, emotional exhaustion, and depersonalization, taking into account the mediating effect of job autonomy. Methodology: To evaluate the research hypotheses, data were collected from 448 people belonging to six organizations in the Colombian electricity sector. Statistical analysis was performed using two structural equation models (SEMs). Results: The results show that a benevolent climate and its three dimensions (friendship, group interest, and corporate social responsibility) mitigate the negative effect of emotional exhaustion and depersonalization. A work environment focused on people and society triggers positive moods that prevent the loss of valuable psychological resources. On the other hand, job autonomy is a mechanism that has a direct impact on the emotional well-being of employees. Therefore, being able to intentionally direct one’s own sources of energy and motivation prevents an imbalance between resources and demands that blocks the potential effect of emotional exhaustion and depersonalization. Practical implications: This study has important practical implications. First, an ethical climate that seeks to build a caring environment needs to strengthen emotional communication among employees through a high perception of support. Second, organizations need to grow and achieve strategic objectives from a perspective of solidarity. Third, a benevolent ethical climate needs to be nurtured by professionals with a clear vocation for service and a preference for interacting with people. Finally, job autonomy must be accompanied by the necessary time management skills. Social implications: This study highlights the importance to society of an ethical climate based on friendship, group interest, and corporate social responsibility. In a society with a marked tendency to disengage from collective problems, it is essential to make decisions that take into account the well-being of others. Originality/value: This research responds to recent calls for more studies to identify organizational contexts capable of mitigating the negative effects of emotional exhaustion and depersonalization. Full article

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article

The integration of artificial intelligence (AI) within the decision-making processes of small- and medium-sized enterprises (SMEs) presents both significant opportunities and substantial ethical challenges. The aim of this paper is to provide a theoretical model depicting the interdependence of organisational decision-making levels and decision-making styles, with an emphasis on exploring the role of AI in organisations’ decision making, based on selected process dimension of the MER model of integral governance and management, particularly in relation to routine, analytical, and intuitive decision-making capabilities. The research methodology employs a comprehensive qualitative analysis of the scientific literature published between 2010 and 2024, focusing on AI implementation in SMEs, ethical decision making in integral management, and regulatory frameworks governing AI use in business contexts. The findings reveal that AI technologies influence decision making across business policy, strategic, tactical, and operative management levels, with distinct implications for intuitive, analytical, and routine decision-making approaches. The analysis demonstrates that while AI can enhance data processing capabilities and reduce human biases, it presents significant challenges for normative–ethical decision making, requiring human judgment and stakeholder consideration. We conclude that effective AI integration in SMEs requires a balanced approach where AI primarily serves as a tool for data collection and analysis rather than as an autonomous decision maker. These insights contribute to the discourse on responsible AI implementation in SMEs and provide practical guidance for leaders navigating the complex interplay between (non)technological capabilities, ethical considerations, and regulatory requirements in the evolving business landscape. Full article

The rapid advancements in healthcare technologies are reshaping the medical landscape, prompting a reconsideration of clinical methodologies and their ethical foundations. This article explores the need for an updated approach to medical deontology, emphasizing the transition from traditional practices to a hybrid clinical methodology that integrates both human expertise and technological innovations. With the increasing use of Artificial Intelligence, data analytics, and advanced medical tools, healthcare professionals are presented with new ethical and professional challenges. These challenges demand a reevaluation of professional responsibility, highlighting the importance of scientific evidence in decision-making while mitigating the influence of economic and ideological factors. By framing medical practice within a systemic and integrated perspective, this article proposes a model that moves beyond the reductionist and anti-reductionist dualism, fostering a more realistic understanding of healthcare. This new paradigm necessitates the evolution of the Medical Code of Ethics, integrating the concept of “medical intelligence” to address the complexities of data management and its ethical implications. The article ultimately advocates for a dynamic and adaptive approach that aligns medical practice with emerging technologies, ensuring that patient care remains person-centered and ethically grounded in a rapidly changing healthcare environment. Full article

The integration of Artificial Intelligence (AI) in construction project management is revolutionising the industry; offering innovative solutions to enhance efficiency, reduce costs, and improve decision making. This structured literature review explored the current applications, benefits, challenges, and future trends of AI in construction project management. This study synthesised findings from 135 peer-reviewed articles published between 1985 and 2024; representing Industry 3.0 (3IR), Industry 4.0 (4IR), and Industry 4.0 Post COVID-19 (4IR PC). Analysis showed that the Planning and Monitoring and Control phases of the project have the greatest application of AI, while decision making, prediction, optimisation, and performance improvement are the most common purposes of AI use in the construction industry. The drivers of AI adoption within the construction industry include technology availability, project outcome and performance improvement, a competitive advantage, and a focus on sustainability. Despite these advancements, the review revealed several barriers to AI adoption, including data integration issues, the high cost of AI implementation, resistance to change among stakeholders, and ethical concerns surrounding data privacy, amongst others. This review also identified future ongoing applications of AI in the construction industry, such as sustainability and energy efficiency, digital twins, advanced robotics and autonomous construction, and optimisation. By providing a comprehensive analysis of the evolution of practices and the future direction of AI application, this study serves as a resource for researchers, practitioners, and policymakers seeking to understand the evolving landscape of AI in construction project management. Full article

Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations. Full article

Brain metastases (BMs) are the most common intracranial tumors in adults. Their heterogeneity, potential multifocality, and complex biomolecular behavior pose significant diagnostic and therapeutic challenges. Artificial intelligence (AI) has the potential to revolutionize BM diagnosis by facilitating early lesion detection, precise imaging segmentation, and non-invasive molecular characterization. Machine learning (ML) and deep learning (DL) models have shown promising results in differentiating BMs from other intracranial tumors with similar imaging characteristics—such as gliomas and primary central nervous system lymphomas (PCNSLs)—and predicting tumor features (e.g., genetic mutations) that can guide individualized and targeted therapies. Intraoperatively, AI-driven systems can enable optimal tumor resection by integrating functional brain maps into preoperative imaging, thus facilitating the identification and safeguarding of eloquent brain regions through augmented reality (AR)-assisted neuronavigation. Even postoperatively, AI can be instrumental for radiotherapy planning personalization through the optimization of dose distribution, maximizing disease control while minimizing adjacent healthy tissue damage. Applications in systemic chemo- and immunotherapy include predictive insights into treatment responses; AI can analyze genomic and radiomic features to facilitate the selection of the most suitable, patient-specific treatment regimen, especially for those whose disease demonstrates specific genetic profiles such as epidermal growth factor receptor mutations (e.g., EGFR, HER2). Moreover, AI-based prognostic models can significantly ameliorate survival and recurrence risk prediction, further contributing to follow-up strategy personalization. Despite these advancements and the promising landscape, multiple challenges—including data availability and variability, decision-making interpretability, and ethical, legal, and regulatory concerns—limit the broader implementation of AI into the everyday clinical management of BMs. Future endeavors should thus prioritize the development of generalized AI models, the combination of large and diverse datasets, and the integration of clinical and molecular data into imaging, in an effort to maximally enhance the clinical application of AI in BM care and optimize patient outcomes. Full article

Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models for creating or enhancing visual data. These generative AI models have shown widespread applications in clinical practice and research. Such applications range from medical documentation and diagnostics to patient communication and drug discovery. These models are capable of generating text messages, answering clinical questions, interpreting CT scan and MRI images, assisting in rare diagnoses, discovering new molecules, and providing medical education and training. Early studies have indicated that generative AI models can improve efficiency, reduce administrative burdens, and enhance patient engagement, although most findings are preliminary and require rigorous validation. However, the technology also raises serious concerns around accuracy, bias, privacy, ethical use, and clinical safety. Regulatory bodies, including the FDA and EMA, are beginning to define governance frameworks, while academic institutions and healthcare organizations emphasize the need for transparency, supervision, and evidence-based implementation. Generative AI is not a replacement for medical professionals but a potential partner—augmenting decision-making, streamlining communication, and supporting personalized care. Its responsible integration into healthcare could mark a paradigm shift toward more proactive, precise, and patient-centered systems. Full article