International Journal of Logistics Research and Applications, Год журнала: 2025, Номер unknown, С. 1 - 28
Опубликована: Март 17, 2025
Язык: Английский
Journal of Clinical Medicine, Год журнала: 2025, Номер 14(5), С. 1605 - 1605
Опубликована: Фев. 27, 2025
Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, societal challenges. Key concerns include data privacy risks, algorithmic bias, regulatory gaps that struggle to keep pace with AI advancements. This study aims synthesize a multidisciplinary framework for trustworthy focusing on transparency, accountability, fairness, sustainability, global collaboration. It moves beyond high-level ethical discussions provide actionable strategies implementing clinical contexts. Methods: A structured literature review was conducted using PubMed, Scopus, Web of Science. Studies were selected based relevance ethics, governance, policy prioritizing peer-reviewed articles, analyses, case studies, guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives clinicians, ethicists, policymakers, technologists, offering holistic “ecosystem” view AI. No trials or patient-level interventions conducted. Results: analysis identifies key current governance introduces Regulatory Genome—an adaptive oversight aligned trends Sustainable Development Goals. quantifiable trustworthiness metrics, comparative categories applications, bias mitigation strategies. Additionally, it presents interdisciplinary recommendations aligning deployment environmental sustainability goals. emphasizes measurable standards, multi-stakeholder engagement strategies, partnerships ensure future innovations meet practical healthcare needs. Conclusions: Trustworthy requires more than technical advancements—it demands robust safeguards, proactive regulation, continuous By adopting recommended roadmap, stakeholders can foster responsible innovation, improve outcomes, maintain public trust AI-driven healthcare.
Язык: Английский
Процитировано
6
Business Strategy and the Environment, Год журнала: 2025, Номер unknown
Опубликована: Фев. 11, 2025
ABSTRACT In the era of rapid technological advancement, generative artificial intelligence (AI) has emerged as a transformative force in various sectors, including environmental sustainability. This research investigates factors and consequences using AI to access information influence green purchasing behavior. It integrates theories such adoption model, value–belief–norm theory, elaboration likelihood cognitive dissonance theory pinpoint prioritize determinants usage for Data from 467 participants were analyzed hybrid methodology that blends partial least squares (PLS) with neural networks (ANN). The PLS outcomes indicate interactivity, responsiveness, knowledge acquisition application, concern, ascription responsibility are key predictors use information. Furthermore, concerns, values, personal norms, responsibility, individual impact, emerge ANN analysis offers unique perspective discloses variations hierarchy these predictors. provides valuable insights stakeholders on harnessing promote sustainable consumer behaviors
Язык: Английский
Процитировано
4
Sustainability, Год журнала: 2024, Номер 16(17), С. 7466 - 7466
Опубликована: Авг. 29, 2024
The purpose of this study is to investigate the role AI capability (AIC) on organizational creativity (OC), green innovation (GI), and sustainable performance (SP). It also aims mediating roles OC GI, as well moderating knowledge sharing culture (KNC). This used quantitative methodology utilized a survey collect data from 421 employees in different organizations Bangladesh. We structural equation modeling (SEM) technique analyze data. finds that significantly influences OC, SP. GI work mediators, KNC serves moderator among suggested relationships. notable for its novelty examining multiple unexplored aspects current body research. research provides valuable insights policymakers practitioners regarding effective integration enhance competitiveness.
Язык: Английский
Процитировано
15
Water Science & Technology, Год журнала: 2024, Номер 90(3), С. 731 - 757
Опубликована: Июль 26, 2024
Artificial intelligence (AI) is increasingly being applied to wastewater treatment enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, major findings of various AI models in the three key aspects: prediction removal efficiency for both organic inorganic pollutants, real-time monitoring essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, conductivity), fault detection processes equipment integral treatment. The accuracy (
Язык: Английский
Процитировано
12
Región Científica, Год журнала: 2025, Номер unknown
Опубликована: Янв. 3, 2025
Studies on artificial intelligence (AI) have increased significantly over the past decade to point that they recently become essential diverse fields. Regarding studies sustainability, environmental care, and application of technological advances, AI-based models also gained particular significance. Accordingly, this study explored relationship between AI, impact through a mixed documentary review, which combined narrative review bibliometric analysis. The examined main ideas stages permeate intersection AI identifying their contributions challenges. analysis provided quantitative overview scientific production, highlighting trends in terms countries, most influential keywords. results reveal has crucial role promoting sustainable practices, but it poses risks require careful consideration. Hence, costs must be analyzed. underlined need for balanced approach maximizes benefits while minimizing its negative impacts environment.
Язык: Английский
Процитировано
2
ACM Computing Surveys, Год журнала: 2025, Номер unknown
Опубликована: Фев. 15, 2025
The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity architectures used. Particularly, machine learning acknowledged as one today’s most energy-intensive computational posing a significant challenge to environmental sustainability next-generation intelligent systems. Achieving entails ensuring that every algorithm designed with mind, integrating green considerations from architectural phase onwards. Recently, Federated Learning (FL), its distributed nature, presents new opportunities address this need. Hence, it’s imperative elucidate potential challenges stemming recent FL advancements their implications for sustainability. Moreover, crucial furnish researchers, stakeholders, interested parties roadmap navigate understand existing efforts gaps green-aware algorithms. This survey primarily aims achieve objective by identifying analyzing over hundred works assessing contributions artificial intelligence sustainable environments, specific focus on IoT research. It delves into current issues federated an energy-efficient standpoint, discussing future prospects application
Язык: Английский
Процитировано
2
LatIA, Год журнала: 2024, Номер 1, С. 12 - 12
Опубликована: Июль 23, 2024
Язык: Английский
Процитировано
7
Sustainable Development, Год журнала: 2024, Номер unknown
Опубликована: Окт. 23, 2024
Abstract This review provides a comprehensive analysis of the intersection between digital sustainability (DS) and eco‐environmental (EES), focusing on opportunities challenges presented by emerging technologies, such as artificial intelligence (AI), blockchain, electric vehicles (EVs), cryptocurrencies. The study critically examines concerns arising from increasing demand for infrastructure depletion essential natural resources, including tantalum, indium, cobalt, lithium. Through an interdisciplinary approach, evaluates ethical, technological, policy implications integrating DS within EES framework. It emphasizes significance innovative governance cross‐sector collaboration to address environmental trade‐offs rebound effects linked with these technologies. Additionally, proposes strategies mitigating ecological impacts transformation identifies crucial research gaps, particularly in resource management long‐term sustainability. findings aim guide alignment EES, fostering more balanced resilient path towards sustainable development. offers actionable insights recommendations industry practitioners, policymakers, researchers committed advancing transformation.
Язык: Английский
Процитировано
7
Región Científica, Год журнала: 2025, Номер unknown
Опубликована: Янв. 3, 2025
This study explores how artificial intelligence (AI) is being used to improve sustainability management and corporate social responsibility (CSR) in Latin America. We analyze the regional context, identify challenges opportunities, present two case studies of IT companies that have implemented AI solutions promote sustainable practices. The findings highlight positive impact on operational efficiency, cost reduction, improved image, while underlining importance a multidisciplinary approach continuous collaboration.
Язык: Английский
Процитировано
1
Sustainable Development, Год журнала: 2025, Номер unknown
Опубликована: Апрель 24, 2025
ABSTRACT This study contributes to the literature on sustainable development by investigating mechanisms through which green finance fosters sustainability in emerging economies. Given increasing importance of artificial intelligence (AI) and renewable energy environmental transitions, we explore their roles as mediators relationship between sustainability. Using a dataset covering 2015–2022, apply Baron Kenny's (1986) mediation approach combined with advanced econometric techniques assess finance's direct indirect effects development. Our findings reveal that directly enhances while significantly promoting AI capacity. However, once these are included, effect weakens, indicating partial effect. Moreover, identifies additional mediating role linking capacity amplifying its overall impact. These results highlight critical interplay finance, AI, achieving economic Policymakers economies should prioritize initiatives, invest AI‐driven clean solutions, support decentralized projects accelerate transitions.
Язык: Английский
Процитировано
1