Artificial Intelligence (AI) has grown significantly in the imaging diagnostics sector, providing advances in diagnostic efficiency. The use of advanced algorithms allows the identification of pathologies quickly and accurately, in some cases surpassing human analysis. In addition, AI enables the automation of repetitive operational tasks, optimizing workflow and service time. This study aims to analyze the opportunities and challenges of implementing AI in the radiology sector, addressing its impact on the practice of radiologists and technologists, as well as its ethical and operational challenges. A systematic review of the literature was carried out, considering articles published between 2015 and 2024. The results demonstrated that AI has significant benefits in radiology, including the automation of exams, the creation of personalized protocols, and the issuance of preliminary reports. However, its implementation presents challenges, such as the lack of transparency in algorithmic decisions and the need for human supervision to minimize diagnostic errors. Furthermore, the responsibility for diagnostic errors continues to lie with radiologists and technologists, requiring new technical skills to adapt to this technology. It is concluded that the incorporation of AI into radiology represents an important advance, but requires ongoing training for its safe and effective implementation. Technology should be seen as a support tool, not as a substitute for human expertise. The success of the integration between artificial and human intelligence will depend on the balance between technological innovation and professional supervision, ensuring safe care aligned with the needs of patients.

The adoption of artificial intelligence systems to define the credit risk score has become an increasingly common situation. However, the adoption of such systems recommends the system to be suitable for the society where it will be used. In this way, the research reflects the importance of adopting multidisciplinary teams for the construction of artificial intelligence systems for credit analysis in order to provide such adequacy. The research problem refers to whether the adoption of professionals from diversified areas for the proper implementation of the artificial intelligence system for credit analysis, with a view to incorporating particularities of the local economy, will allow for a better adjustment of the risk assumed in each operation. The investigation to answer the problem focused on situations where risk analysis failed to capture particularities of local markets, generating inadequacy in risk analysis and exposing the financial institution to unwanted situations. As a conclusion of the research, it was observed that the implementation of artificial intelligence systems for credit analysis exposes the financial institution to new risks, deserving such situation an appropriate governance structure endowed with instruments that allow the adaptation of the model to the society that seeks to define the risk.

Scientific reproducibility has become a central theme amidst the crisis affecting various fields of knowledge, especially in the era of artificial intelligence (AI). This paper analyzes the technical, legal, and ethical challenges related to reproducibility, focusing on the interaction between AI, intellectual property, copyrights, patents, and the Brazilian General Data Protection Law (LGPD). The impact of AI on study replication, legal barriers to data sharing, and strategies to reconcile scientific transparency with privacy protection are addressed. The analysis highlights the need for integrated public policies, adequate technological infrastructure, and a scientific culture that values reproducibility as a fundamental ethical principle for responsible innovation. It concludes that the advancement of reproducible science depends on multidisciplinary cooperation, balancing legal protection with open access, and ethical use of new technologies.  

This paper addresses the design of a system to facilitate individual environmental risk assessment and long-term risk management in the Industry 4.0 work context. The solution is based on IoT to provide data related to workers’ exposure to hazardous agents (dust, noise, ultraviolet radiation, poor lighting, and inappropriate temperature and humidity) through a simple interface for employees and employers. The system includes a monitoring device and a server and performs employee registration, receives secure messages from the monitoring devices, shows information about the workers’ exposure, and triggers alarms when the measures reach or exceed the limits established by applicable legislation and/or standards. The system was tested in a controlled environment, and the results are presented in this work. Our solution is still under development, and in future stages, it will include a smartphone app for employees to check their exposures and the use of artificial intelligence on the server, which is expected to enable long-term planning by companies based on the analysis of employees’ health history and their exposures to hazardous agents.

The deployment of fifth-generation wireless communications (5G) networks brought a significant difference in the data rate and throughput to the wireless systems. It ensures ultra-low latency and high reliability. In particular, Network Slicing (NS), one of the enablers for the 5G phase-II and beyond, has opened enormous opportunities for the Communications Service Provider (CSPs). NS allows CSPs to create independent virtual networks in the same physical network to guarantee high service levels. This paper provides an overview of the advances in NS from the perspective of the business opportunities and associated standardization activities. Standardization is critical in research as it intends to maintain interoperability among multi-vendor scenarios in telcos. We emphasize highlighting the technical facets of slicing within the business implementation and industry standardization process. Additionally, we address the application of Artificial Intelligence (AI) and Machine Learning (ML) to NS-enabled future networks deployments. A set of use cases and the underlying specific requirements challenges are discussed as well. Finally, future research directions are addressed in detail.

The influence of algorithms on society is increasing due to their growing presence in all areas of daily life. Although we are not always aware of it, they sometimes usurp the identity of other social actors. The main purpose of this article is to address the meta-research on the field of artificial intelligence and communication from a holistic perspective that allows us to analyze the state of academic research, as well as the possible effects on these areas and on the democratic system. To this end, we carried out a systematized review of recent literature using quantitative and qualitative approaches. The subject analyzed is changing and novel: it includes the impact and interaction of algorithms, bots, automated processes, and artificial intelligence mechanisms in journalism and communication, as well as their effects on democracy. The results show expanding scientific production, mostly in English, based on theoretical discussion or focused on the perception of communication professionals. The object of study is centered mostly on journalism and democracy, and to a lesser degree on ethics or education. Studies indicate great interest in the effects of the use of algorithms on journalism and democracy, but the answers are still uncertain and the challenges for the coming years are significant

In this study, we focused on analyzing customer-generated data on Facebook to explore how textual content on a social web can provide valuable information for decision support. To accomplish this goal, we used several techniques that included social network analysis (SNA), natural language processing (NLP), data mining (DM), and machine learning (ML), integrating them with artificial intelligence approaches. Our analysis aimed to harness the information generated during the Volkswagen pollutant emissions situation in a case study that was conducted using the textual content from 10,642 posts, that represented the interactions of 25,877 users over a span of twenty-two weeks. The results demonstrated that monitoring online social networks (OSNs) can significantly enhance decision-making processes and might help to mitigate potential damages to brands/businesses. By leveraging the proposed methodological approach, a set of orientations for decision-making was extracted, providing valuable guidance for brand management and reputation protection. Overall, this study highlights the importance of analyzing textual content on OSNs and leveraging advanced computational techniques to improve decision support.

Industry 5.0 presents itself as a strategy that puts the human factor at the centre of production, where the well-being of the worker is prioritized, as well as more sustainable and resilient production systems. For human centricity, it is necessary to empower human beings and, respectively, industrial operators, to improve their individual skills and competences in collaboration or cooperation with digital technologies. This research’s main purpose and distinguishing point are to determine whether Industry 5.0 is truly human-oriented and how human centricity can be created with Industry 5.0 technologies. For that, this systematic literature review article analyses and clarifies the concepts and ideologies of Industry 5.0 and its respective technologies (Artificial Intelligence, Robotics, Human-robot collaboration, Digitalization), as well as the strategies of human centricity, with the aim of achieving sustainable and resilient systems, especially for the worker.

Dynamic stall is an aerodynamic regime characterized by loss of airfoil lift, drag increment, and abrupt changes in the pitching moment. Such effects can couple with structural dynamics where perturbations can be easily amplified, making this a critical phenomenon that jeopardizes operational safety. Hence, there is always the need to constantly study the basics of dynamic stall and provide newer predictive models that can take advantage of the current interest peak in artificial intelligence. The present work builds upon that need, exploring the ability of a simple feed-forward network to predict the oscillation cycle of a pitching airfoil experiencing from light to deep stall of a NACA0012 airfoil close to a Reynolds number of approximately 1.1x10^6. The proposed neural network uses the angle of attack and its rate of change as inputs, then estimates the whole aerodynamic cycle at once, outputting an aggregated vector of drag, lift, and pitching moment coefficients. The training phase was conducted using a database containing several conditions obtained from experimental tests, with a strict convergence criterion of R^2=0.99 for both training and test datasets. Results show that the neural network, even in the least-performing conditions, can capture the aerodynamics and overall tendencies, even if some dynamics are underrepresented in the training dataset. The present work brings down the complexity of methodology while demonstrating that a simplistic architecture can still offer an accurate dynamic stall model.

Introduction: Yearly around 21 thousand adult patients visit our tertiary hospital’s emergency department after suffering from high or low energy trauma. Skeletal radiographs, being inexpensive and widely available, are the first‐line imaging mo‐ dality. Recent studies are showing encouraging results of the use of artificial intelligence in the detection of bone fractures. The main objective of this study is to compare the diagnostic accuracy between a medical‐grade artificial intelligence (AI) software (BoneView®, Gleamer) and orthopaedic surgeons of various levels of expertise for the detection of bone fractures in a tertiary hospital’s emergency department. Methods: Retrospective analysis of a series of posttraumatic radiographic examinations, including only adult patients with plain radiographs of limbs or pelvis obtained after a recent trauma. Exclusion criteria were patients with cast control radiographs, images with inadequate radiographic quality, and examinations showing only obvious fractures. The diagnostic performance of the AI software and six orthopaedic surgeons was measured by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The AI software had 91.3% sensitivity (95% CI: 82.03‐96.74) and 97.3% specificity (95% CI: 93.22‐99.26), with 0.95 AUC (95% CI: 91.3‐98.8; p <0.001). All six readers had inferior results in every measure obtained, with slight differences between them. Conclusion: Our study demonstrated that the BoneView® software has a high diagnostic capacity for fractures and, in this regard, can be considered a useful tool in the emergency department.