Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
This peer-reviewed conceptual analysis examines why small and medium-sized enterprises struggle to adopt AI despite its transformative potential. Using the technology-organization-environment framework combined with diffusion of innovations attributes, it identifies ten critical challenges across data access, skills, culture, infrastructure, and governance, and pairs them with context-sensitive solutions.
As of its July 2025 publication, this peer-reviewed review examined how trust in AI systems can be systematically measured, analyzing frameworks including the NIST AI Risk Management Framework, the AI Trust Framework and Maturity Model, and ISO/IEC standards around fairness, transparency, privacy and security.
This peer-reviewed review from July 2025 examines how Digital Twins that are continuously updated by real-world data, when coupled with Artificial Intelligence, are being applied to healthcare, with emphasis on movement rehabilitation over the past seven years. It reports that this combination is reshaping care by streamlining diagnostic workflows, improving disease management, and enabling experimentation and predictive modeling without direct patient risk.
As of July 28 2025, this peer-reviewed review synthesized machine learning and statistical approaches for forecasting and classifying water quality, focusing on hybrid models that combine multiple methods. It assessed their application to rivers in Malaysia facing pollution from industrialisation, agriculture, and urban expansion, and reviewed standards and interpretability techniques.
What happened is that by 2026-06-03 scholars observed that smart technology, AI, robotics and algorithms were changing work design, with reviews noting varied effects on performance and wellbeing and primary emphasis on displacement of routine tasks and the need to upskill and reskill workers.
By July 2026, researchers examined singing data collection as AI voice synthesis advanced, analyzing three singing datasets with the Ethically Aligned Stakeholder Elicitation framework. They found data-contributors have event-centric roles with minimal authority over licensing and access, while data-collectors retain control.
By July 2026 this peer-reviewed synthesis examined how artificial intelligence reshapes cybersecurity in the Asia-Pacific, focusing on ASEAN. It catalogued offensive methods such as data poisoning and model extraction and defensive uses of machine learning and deep learning for anomaly detection and incident prioritization, alongside technical controls like sandboxing and identity controls.
This 2026 peer-reviewed perspective surveys ethical concerns from widespread AI adoption as they relate to human health. It reviews risks of large-scale AI systems, corporate and governmental applications, patient use of AI, and clinical uses split into passive tasks like recording documents and active tasks like diagnosing and prescribing, ending with discussion of reporting, responsibility, and regulation.
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