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Artificial Intelligence in Allied Health Practice: Current Evidence, Emerging Applications, And Implications for Patient Safety

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Artificial intelligence (AI) is moving from experimental research into routine healthcare workflows, including diagnostic support, clinical documentation, rehabilitation, laboratory medicine, medical imaging, patient education, and service management. Allied health professionals are increasingly involved in these AI-enabled pathways, yet evidence and implementation guidance are often concentrated in physician-led specialties. This narrative review synthesises current evidence on the use of AI across allied health practice and examines implications for patient safety, professional accountability, education, and health equity. Literature from peer-reviewed journals and authoritative guidance was considered, with emphasis on clinical applications, implementation barriers, bias, explainability, human-AI interaction, and generative AI. Current evidence supports potential benefits such as workflow support, image interpretation, pattern recognition, personalised rehabilitation, laboratory decision support, documentation assistance, and improved access to health information. However, technical performance alone does not establish clinical effectiveness or safety. Dataset shift, hidden bias, automation bias, inaccurate generated content, privacy risks, poor interoperability, insufficient local validation, and unclear responsibility can introduce new failure modes. Safe adoption should include a defined intended use, representative validation, transparent performance monitoring, user training, incident reporting, privacy safeguards, human oversight, and periodic re-evaluation after deployment. The review concludes that AI has meaningful potential across allied health practice, but sustainable adoption depends on evidence-based governance and continuous patient-safety monitoring rather than technology adoption alone.

From Gatekeepers to Gatekeeping Systems: Rethinking Journalism Theory in the Age of Algorithms and Generative AI

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Gatekeeping has remained one of the foundational theoretical approaches for explaining how information becomes news. From Kurt Lewin’s conceptualisation of gates and channels to David Manning White’s study of the newspaper wire editor, traditional gatekeeping theory located considerable power in human actors who selected, rejected and organised information before it reached audiences. The contemporary news environment, however, complicates this model. News selection is increasingly distributed across journalists, newsroom analytics, search engines, social media platforms, recommender systems and generative artificial intelligence. This paper re-examines gatekeeping theory through an interdisciplinary synthesis of journalism studies, platform studies, artificial intelligence research and communication theory. Drawing on a structured conceptual review of more than 29 major scholarly contributions, it argues that contemporary journalism is moving from identifiable gatekeepers towards interconnected gatekeeping systems. Within these systems, editorial judgement has not disappeared; rather, it interacts continuously with computational selection, platform infrastructures, audience behaviour and generative systems capable of producing, summarising and reorganising information. The paper proposes a five-layer model of contemporary gatekeeping comprising source, editorial, algorithmic, platform and generative gates. It further distinguishes between selection power, traditionally exercised by editors, and visibility power, increasingly exercised through technological infrastructures. The study concludes that gatekeeping theory remains valuable but requires conceptual expansion from the question of who controls the gate to the broader question of how interconnected human and computational systems determine what information becomes visible, credible and socially consequential.

Artificial Intelligence and Mechanical Engineering in Greek Vocational High Schools: Educational Perspectives and Institutional Barriers

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This scoping review investigates the potential and challenges of integrating Generative Artificial Intelligence (GenAI) into Vocational Education and Training, with a specific focus on the Mechanical Engineering sector of Greek Vocational High Schools (EPA.L.). The purpose of this study is to analyze how AI can transform the teaching of mechanical engineering courses, bridging the gap between theoretical knowledge and laboratory practice in the context of Industry 4.0. The methodology involved a literature review of 26 scientific sources (2016-2026), utilizing thematic analysis guided by theoretical frameworks such as the Technology Acceptance Model (TAM) and teacher self-efficacy. Key findings indicate that the use of specialized, knowledge-enhanced Large Language Models (LLMs) significantly improves the structural integrity and technical accuracy of instructional materials, effectively reducing teacher preparation time. However, despite a high perceived usefulness, the adoption of AI by educators in public schools is severely hindered by the lack of adequate laboratory infrastructure, the absence of discipline-specific training, and heightened AI anxiety regarding ethical risks. In conclusion, the transition to an AI-enhanced school laboratory necessitates immediate upgrades to equipment and a shift from general ICT training to targeted, specialty-specific professional development programs, ultimately empowering teachers to become designers of digital learning experiences.

Artificial Intelligence and Human Capital in The Context of Economic Security and Sustainable Development of Tourism

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In the context of increasing global economic instability, accelerated digitalization and increased security requirements, the tourism industry is faced with the need for a radical transformation of management models and the focus of this study is the interaction between intelligent technologies and human capital in the context of risk management, security and organizational resilience. For this purpose, the role of artificial intelligence and human capital as key factors for economic security and sustainable development of tourism is analyzed. Through a theoretical and conceptual approach, the study explores good practices for the integration of artificial intelligence with human capital, contributing to stability, competitiveness and long-term sustainability in the tourism industry. The article argues that sustainable tourism development requires a balanced approach, in which artificial intelligence serves as a supporting tool, while human capital retains its central role in strategic decision-making, ethical management and security management in tourism development. Through a theoretical and conceptual approach and a study of good practices.

AI-Powered Token Prediction and Automated Trading in Web3 Using On-chain Data and Decentralized Exchanges

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This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries.  LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.

Assessment of Artificial Intelligence (AI) on Media Practitioners’ Creativity and Capacity Development

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Artificial intelligence (AI) is an innovative and modern technology that enables computers to perform tasks that typically require human-like intelligence. In January, 2025, empirical report showed that Nigeria surpasses global average with 70% AI adoption rate. The report said Nigeria’s online population is leading the global adoption of generative AI, with 70 percent of respondents reporting usage, far exceeding the global average of 48 percent. With this report, Nigerians dependence on AI is quite alarming and it is of believe that AI is gradually taken over human creativity and existence.

The study adopted survey method and in-depth Interview research methods with a focus on 120 respondents for the survey method and three (3) media practitioners for the in-depth-interview that cut across print, broadcast and online media in three selected states (Osun, Oyo and Lagos) in South-West, Nigeria. One editor from print media outfits, one editor from broadcast media and one respondent from online news outlets who were purposively selected based on their knowledge and experience in the field of journalism.

The study found out that AI has come to stay in modern world and it is very essential in different fields most especially field of journalism. Finding also indicated that when applied improperly or excessively, AI increases the efficiency and some of the skills but poses a threat to originality, depth, and standards.

The study therefore concludes that AI is a strong supporting system but not a substitute of human journalists. A balanced integration, ethical standards, and long-term capacity building can help the Nigerian media to embrace the benefits of AI and remain creative, accurate, and trusted by the people. The study recommends that media organizations ought to ensure that all their staff are trained on the ways of using AI intelligently, as an assistant and not a thinking substitute.

Exemplary Model of AI-Supported Adaptive Optimization Energy Flow Control in Smart City Microgrids: A Simulation-Based Scenarios

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The paper focuses on the possibilities for developing a model for adaptive control of electricity flows in urban microgrids using AI support into the Internet of Things networks.  The goal is the requirement for smarter, more adaptive and sustainable methods in controlling local energy systems. This is critical for distributed generation and the growing incorporation of renewable energy resources. The study is conceptual in nature and aims to develop an integrated model that combines physical energy infrastructure, IoT-based data acquisition, the analytical capabilities of artificial intelligence, and a logic for adaptive real-time decision-making. It is analyzed the theoretical foundations of adaptive management in microgrids, the design of model development of multilayered architecture, and the interaction between physical and information flows. Particular attention is given to the role of intelligent monitoring devices, forecasting and optimization algorithms, as well as the coordination between local generation, storage, consumption, and exchange with the main grid. The proposed model is analyzed through comparison with traditional, optimization-based, and AI-driven models discussed in the scientific literature, and it is argued that the integration of AI and IoT enables higher adaptability, improved load balancing, more efficient use of local energy resources, and better integration of renewable energy sources in the urban energy environment. The proposed model provides a conceptual framework for the intelligent management of electricity flows in urban microgrids, emphasizing its potential for further development and application in sustainable energy systems.

Application of Artificial Intelligence (AI) in Learning among Pre-service Teachers at Thu Dau Mot University: Current Status, Opportunities, and Challenges in the Context of Digital Transformation

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In the context of digital transformation in higher education, artificial intelligence (AI) is increasingly being utilized by pre-service teachers to support their learning. This study aims to analyze the current status, opportunities, and challenges of AI application at Thu Dau Mot University. A mixed-methods approach was employed, including a survey of 412 students and semi-structured interviews with 3 lecturers, 2 administrators, and 5 students.

The findings indicate that AI is widely used for information retrieval, learning support, and content generation, thereby enhancing learning effectiveness, fostering self-directed learning, and enabling personalized learning processes. However, several challenges remain, including over-reliance on technology, insufficient information evaluation skills, and risks related to academic integrity.

Based on these findings, the study proposes several recommendations to improve the effective integration of AI in teacher education, contributing to meeting the demands of digital transformation in higher education.

A Conceptualized Framework of Ethical and Responsible Use of Artificial Intelligence Tools in Higher Education Ecosystem

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This study presents results of a systematic literature review (SLR) of the responsible use of artificial intelligence (AI) tools in higher education, identify patterns of ethical and irresponsible use, and propose a conceptual framework for predicting ethical AI adoption. Following PRISMA guidelines, was conducted on 60 peer-reviewed studies published between 2022 and 2026, sourced from Google Scholar. Studies were mapped against four research questions addressing AI tools used, their applications, reported unethical practices, and predictive modelling approaches. Results reveal that general AI, generative AI tools, and large language models dominate higher education contexts, primarily deployed for personalized learning, academic work, and teaching. Irresponsible practices were documented in one-third of studies, including academic integrity breaches (13.33%), algorithmic bias,  and privacy violations. Critically, no existing study developed a real-time predictive model capable of monitoring ethical AI use, despite four studies demonstrating predictive modelling capabilities for other purposes. This study addresses a significant gap by proposing a novel conceptual framework that integrates AI tool deployment, user behaviour, governance measures, and predictive analytics to forecast ethical outcomes. The framework provides higher education institutions with a pathway toward data-informed, proactive governance of AI technologies.

Artificial Intelligence and Automation in Hospital Administrative Systems: A Scoping Review

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Background: Hospital administrative processes including billing, scheduling, and medical records management—are critical to health system performance but are often characterized by inefficiencies, high operational costs, and workforce burden. Artificial intelligence (AI) and automation technologies, including robotic process automation (RPA) and natural language processing (NLP), have emerged as potential solutions to streamline these processes and enhance productivity.

Objective: This scoping review aimed to synthesize existing evidence on the use of AI and automation in hospital administrative functions, focusing on efficiency gains, cost savings, implementation barriers, and ethical and regulatory considerations.

Methods: A scoping search of peer-reviewed literature was conducted across major electronic databases including PubMed, Scopus, Web of Science, and Google Scholar. Studies published between 2015 and 2025 that examined AI-based or automation-driven interventions in hospital administrative settings were included. Eligible studies addressed applications in billing, scheduling, records management, hospital information systems, or workflow optimization. Data was extracted and synthesized narratively due to heterogeneity in study designs and outcome measures.

Results: The review identified substantial evidence that AI and automation improve administrative efficiency through reduction of processing time, minimization of manual errors, and optimization of resource allocation. RPA demonstrated significant benefits in billing and claims processing, while NLP enhanced documentation accuracy and records retrieval. Several studies reported measurable cost savings and productivity improvements following implementation. However, common barriers included integration challenges with legacy systems, limited interoperability, data quality concerns, staff resistance, insufficient training, high upfront costs, and uncertain short-term return on investment. Regulatory and governance challenges, particularly data protection compliance and algorithm transparency were also frequently highlighted.

Conclusion: AI and automation technologies show considerable promise in transforming hospital administrative processes by improving efficiency and reducing operational costs. Nevertheless, successful implementation requires strong governance frameworks, workforce capacity building, financial planning, and ethical oversight. Future research should focus on longitudinal cost-effectiveness evaluations and context-specific implementation strategies, particularly in resource-limited health systems.