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Business Ethics As a Competitive Advantage in the Era of Digital Technologies
(Springer Nature Switzerland, 2025)
Casas-Martínez, María de la Luz
We are in the era of digital technologies, which leads us to a deep reflection on its scope and challenges from an ethical perspective. All new knowledge is desirable because it is a product of intelligence, but science is applied to reality by humans, and as such has consequences that must be assessed within the framework of values. The purpose of this chapter is to articulate a reasoned defense of the proposition that Digitalization, Innovation, and Sustainability in Organizations represent a multifaceted reality that imperatively requires, for its legitimate fulfillment, adherence to principles of personal and corporate ethics. The objective of this reflection is focused on the consideration of the human being as an ethical entity and his congruent acting in all spheres of his life, which includes work, then the particularities of ethics in the digital era are considered, considering the UNESCO guidelines on digital technology. The methodological approach of this analysis is qualitative, based on introspection and critical evaluation of recognized sources in the fields of philosophy, ethics, bioethics, and the business sector. It concludes with the consideration of the business valuation of ethical acting as a competitive advantage in this digital era. ©The author ©Springer.
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Digital Transformation and Innovation in Organizations : A Latin American Perspective
(Springer Nature Switzerland, 2025)
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Antonia Teran-Bustamante
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This volume discusses the intersection of digital transformation and innovation in firms, sectors, and regions in Latin America. It addresses the region’s labor market challenges in the advent of the digital era and the influences of AI. The chapters cover topics ranging from education, organizational culture, sustainability, ethics, and human resources. Exploring how digital and STEM literacies can serve as a tool for developing skills in organizations and emphasizing the need for human adaptability in the context of Industry 5.0, this book provides scholars with case studies to better understand the ongoing debates on labor market challenges. ©The authors ©Springer.
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Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review
(JMIR Publications Inc., 2025)
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Noguez, Julieta
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Magana, Alejandra J.
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Benes, Bedrich
Background: Artificial intelligence (AI) has introduced novel opportunities for assessment and evaluation in surgical training, offering potential improvements that could surpass traditional educational methods. Objective: This scoping review examines the integration of AI in surgical training, assessment, and evaluation, aiming to determine how AI technologies can enhance trainees’ learning paths and performance by incorporating data-driven insights and predictive analytics. In addition, this review examines the current state and applications of AI algorithms in this field, identifying potential areas for future research. Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, the PubMed, Scopus, and Web of Science were searched for studies published between January 2020 and March 18, 2024. Eligibility criteria included English-language full-text articles that investigated the application of AI in surgical training, assessment, or evaluation; non-English texts, reviews, preprints, and studies not addressing AI in surgical education were excluded. After duplicate removal and screening, 56 studies were included in the analysis. Data were structured by categorizing studies according to surgical procedure, AI technique, and training setup. Results were synthesized narratively and summarized in frequency tables. Results: From 1400 initial records, 56 studies met the inclusion criteria. Most were journal articles (84%, 47/56), with the remainder being conference papers (16%, 9/56). AI was most frequently applied in minimally invasive surgery (27%, 15/56), neurosurgery (20%, 11/56), and laparoscopy (16%, 9/56). Common techniques included machine learning (20%, 11/56), clustering (14%, 8/56), deep learning (11%, 6/56), convolutional neural networks (11%, 6/56), and support vector machines (11%, 6/56). Training setups were dominated by simulation platforms (33%, 19/56) and box trainers (24%, 13/56), followed by surgical video analysis (16%, 9/56), and robotic systems such as the da Vinci platform (13%, 7/56). Across studies, AI-enhanced training environments provided automated skill assessment, personalized feedback, and adaptive learning trajectories, with several reporting improvements in trainees’ learning curves and technical proficiency. However, heterogeneity in study design and outcome measures limited comparability, and algorithmic transparency was often lacking. Conclusions: The application of AI in surgical training demonstrates the potential to enhance skill acquisition and support more efficient, personalized, and adaptive learning pathways. Despite encouraging findings, several limitations exist, including small sample sizes, the lack of standardized evaluation metrics, and insufficient external validation of AI models. Future studies should aim to clarify AI methodologies, improve reproducibility, and develop scalable, simulation-based solutions aligned with global education goals. ©The authors ©JMIR Publications Inc.
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Thematic mapping of artificial intelligence in management: A bibliometric approach using co-word analysis (2015–2024)
(Pro-Metrics, 2025)
Salgado-García, Jorge Arturo
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Objective: The objective of this study was twofold: first, to map the main themes in the literature on artificial intelligence in management, and second, to explore the relationships between these themes. Design/Methodology/Approach: A co-word analysis was performed on 15,835 articles indexed in Scopus (2015–2024), with the author’s keywords in the field of administration constituting the unit of analysis. The semantic network under consideration was constructed using the 50 most frequent terms, applying normalization by association and the Walktrap algorithm for cluster detection. Results/Discussion: The results of the analysis indicated that the extant literature was organized around three thematic groups. The first of these focused on conversational interfaces, the second on digital transformation, and the third adopted a computational approach. The thematic structure identified reflected a field in the process of consolidation, with a predominance of technical approaches and limited functional specialization. Conclusion: Contemporary research endeavors prioritized methodological development over strategic implementation in particular organizational contexts. These findings underscored the necessity for more comprehensive approaches that articulated technology, management, and governance. Moreover, they called for a future agenda that was oriented toward its adoption from sociotechnical perspectives. ©The authors ©Iberoamerican Journal of Science Measurement and Communication (Revista Iberoamericana de Medición y Comunicación de la Ciencia) ©Pro Metrics.
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Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics
(MDPI, 2025)
Barroso, Ricardo Alexandre
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Agüero-Chapin, Guillermin
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Sousa, Rita
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Antunes, Agostinho
Overcoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the exploration of marine-derived antimicrobial peptides (AMPs) for innovative solutions. Cnidarians, such as corals, sea anemones, and jellyfish, are a promising valuable resource of these bioactive peptides due to their robust innate immune systems yet are still poorly explored. Hence, we employed an in silico proteolysis strategy to search for novel AMPs from omics data of 111 Cnidaria species. Millions of peptides were retrieved and screened using shallow- and deep-learning models, prioritizing AMPs with a reduced toxicity and with a structural distinctiveness from characterized AMPs. After complex network analysis, a final dataset of 3130 Cnidaria singular non-haemolytic and non-toxic AMPs were identified. Such unique AMPs were mined for their putative antibacterial activity, revealing 20 favourable candidates for in vitro testing against important ESKAPEE pathogens, offering potential new avenues for antibiotic development. ©The authors. ©MDPI.
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Determining the medical Spanish translation capabilities of three artificial intelligence translation models for Mohs micrographic surgical instructions
(Elsevier Inc., 2024)
Scheinkman, Ryan
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Montoya, Sofia
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Náder, Maria
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Ramírez, Mariana
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Barbato, Kristiana
To the Editor: Artificial intelligence (AI) has been used to simplify medical-legal documentation.1 In order to protect patients from mistranslations, it is critical to assess the accuracy of AI translations. We attempted to assess the current translational capacities of 3 AI models for Mohs micrographic surgery documentation. The purpose of this analysis was to see if these programs had capabilities that were comparable to human medical translators and determine their capacity for future medical translation applications. In order to determine the validity of these models, preoperative and postoperative instructions from multiple sources were translated by Google Translate, Amazon Translate, and DeepL to Spanish from 3 publicly available academic center websites, specifically: the University of Mississippi Medical Center (University of Mississippi), University of Rochester, and Brigham Cancer Center.2-5 Accuracy of translation was then assessed by 3 native Spanish-speaking medical professionals and students that received C-1 levels on the Test of English as a Foreign Language demonstrating advanced English proficiency. ©The authors © Journal of the American Academy of Dermatology ©Elsevier Inc.
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Using Social Robotics to Identify Educational Behavior: A Survey
(MDPI, 2024)
Romero-C. de Vaca, Antonio J.
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Melendez-Armenta, Roberto Angel
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The advancement of social robots in recent years has opened a promising avenue for providing users with more accessible and personalized attention. These robots have been integrated into various aspects of human life, particularly in activities geared toward students, such as entertainment, education, and companionship, with the assistance of artificial intelligence (AI). AI plays a crucial role in enhancing these experiences by enabling social and educational robots to interact and adapt intelligently to their environment. In social robotics, AI is used to develop systems capable of understanding human emotions and responding to them, thereby facilitating interaction and collaboration between humans and robots in social settings. This article aims to present a survey of the use of robots in education, highlighting the degree of integration of social robots in this field worldwide. It also explores the robotic technologies applied according to the students’ educational level. This study provides an overview of the technical literature in social robotics and behavior recognition systems applied to education at various educational levels, especially in recent years. Additionally, it reviews the range of social robots in the market involved in these activities. The objects of study, techniques, and tools used, as well as the resources and results, are described to offer a view of the current state of the reviewed areas and to contribute to future research. ©The authors ©MDPI.
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Preface : Advances in Soft Computing : 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13–18, 2023, Proceedings, Part II
(Springer, 2024-01-01)
Calvo, Hiram
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The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society for Artificial Intelligence (SMIA) since 2000. MICAI is a major international artificial intelligence (AI) forum and the main event in the academic life of the country’s growing AI community. This year, MICAI 2023 was graciously hosted by the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) and the Universidad Autónoma del Estado de Yucatán (UAEY). The conference presented a cornucopia of scientific endeavors. ©Springer.
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Artificial Intelligence and Its Application in the Study of the Legal Complexity of the Value Added Tax Act in Mexico
(2022)
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Carriles Álvarez, Alonso
The text is a raw material, researchers need to extract information and patterns of value. Through the use of AI tools in conjunction with the hard sciences, it is now possible to access significant sources of knowledge that previously remained hidden in the form of patterns of ideas and feelings stored in large volumes of text. The analysis of the raw text of the Law of Value-Added Tax (VAT) considered the three elements: structure, language, and interdependence. With these three elements, a legal complexity index was constructed, and the results of the model’s parameters show the following: the value for the legal complexity variable was negative (−1.39), which means that when the legal complexity index per unit increases, tax collection will decrease 1.39%. It is helpful to remember that interdependence is the component that outweighs the rest within the legal complexity index. The GDP estimator showed a positive sign, and its magnitude was 4.51; this means that when this estimator increases 1%, VAT collection could increase a 4.5%. © 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Feature Selection Methods Evaluation for CTR Estimation
(2016)
Miralles-Pechuán, Luis
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The most widespread payment model in online advertising is Cost-per-click (CPC). In this model the advertisers pay each time that a user generates a click. In order to enhance the income of CPC Advertising Networks, it is necessary to give priority to the most profitable adverts. The most important factor in the profitability of an advert is Click-through-rate (CTR), which is the probability that a user generates a click in a given advert. In this paper we find which feature selection method between PCA, RFE, Gain ratio and NSGA-II is better suited, or if otherwise, the machine learning classification methods work best without any feature selection method. ©2016 IEE
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