Impact Of Artificial Intelligence Research Articles

Impact of Artificial Intelligence on the Sphere of the Judiciary and the Perspectives for Future Research

Impact of Artificial Intelligence on the Sphere of the Judiciary and the Perspectives for Future Research

The Impact of Artificial Intelligence on Classroom Teaching in Universities

The Impact of Artificial Intelligence on Classroom Teaching in Universities

A Review of Research on the Impact of Artificial Intelligence on Learning

A Review of Research on the Impact of Artificial Intelligence on Learning

Artificial Intelligence Implementation in Agile Project Management Addressing Challenges and Maximizing Impact

The Agile methodology, with an 80% adoption rate, often faces challenges leading to project failures. This study investigates using artificial intelligence (AI) to overcome these challenges through a systematic literature review of 44 papers. It examines AI's impact on key Agile phases: envision, speculate, explore, adapt, and close. Findings highlight AI's critical role in improving project outcomes by addressing implementation challenges. AI tools aid in risk assessment and project selection during planning, enhance effort estimation and task allocation in speculation, improve team communication and technical issue resolution in exploration, optimize systems in adaptation, and provide valuable insights in closure. The paper offers guidance on effective AI integration to enhance Agile Project Management success.

Correction: The Impact of Artificial Intelligence on Health Equity in Dermatology

Correction: The Impact of Artificial Intelligence on Health Equity in Dermatology

Ethical Considerations in Artificial Intelligence Interventions for Mental Health and Well-Being: Ensuring Responsible Implementation and Impact

AI has the potential to revolutionize mental health services by providing personalized support and improving accessibility. However, it is crucial to address ethical concerns to ensure responsible and beneficial outcomes for individuals. This systematic review examines the ethical considerations surrounding the implementation and impact of artificial intelligence (AI) interventions in the field of mental health and well-being. To ensure a comprehensive analysis, we employed a structured search strategy across top academic databases, including PubMed, PsycINFO, Web of Science, and Scopus. The search scope encompassed articles published from 2014 to 2024, resulting in a review of 51 relevant articles. The review identifies 18 key ethical considerations, including 6 ethical considerations associated with using AI interventions in mental health and wellbeing (privacy and confidentiality, informed consent, bias and fairness, transparency and accountability, autonomy and human agency, and safety and efficacy); 5 ethical principles associated with the development and implementation of AI technologies in mental health settings to ensure responsible practice and positive outcomes (ethical framework, stakeholder engagement, ethical review, bias mitigation, and continuous evaluation and improvement); and 7 practices, guidelines, and recommendations for promoting the ethical use of AI in mental health interventions (adhere to ethical guidelines, ensure transparency, prioritize data privacy and security, mitigate bias and ensure fairness, involve stakeholders, conduct regular ethical reviews, and monitor and evaluate outcomes). This systematic review highlights the importance of ethical considerations in the responsible implementation and impact of AI interventions for mental health and well-being. By addressing privacy, bias, consent, transparency, human oversight, and continuous evaluation, we can ensure that AI interventions like chatbots and AI-enabled medical devices are developed and deployed in an ethically sound manner, respecting individual rights, promoting fairness, and maximizing benefits while minimizing potential harm.

AI Meets the Shopper: Psychosocial Factors in Ease of Use and Their Effect on E-Commerce Purchase Intention.

The evolution of e-retail and the contribution of artificial intelligence in improving algorithms for greater customer engagement highlight the potential of these technologies to develop e-commerce further, making it more accessible and personalized to meet individual needs. This study aims to explore the psychosocial factors (subjective norms; faith; consciousness; perceived control) that affect AI-enabled ease of use and their impact on purchase intention in online retail. We will also assess the mediating effect of AI-enabled ease of use between psychosocial factors and consumer purchase intention. A quantitative methodology was used, and 1438 responses were collected from Portuguese consumers on e-retail. Structural equation modeling was used for the statistical treatment. The findings indicate that subjective norms do not positively impact AI-enabled ease of use, whereas factors such as faith, consciousness, and perceived control do enhance it. Furthermore, AI-enabled ease of use itself boosts purchase intention. Additionally, the effects of subjective norms, faith, consciousness, and perceived control on purchase intention are significantly enhanced when mediated by AI-enabled ease of use, highlighting the crucial role of usability in shaping consumer purchase behavior. The contribution of this study has been made through the formulation model that provides a systematized perspective about the influencers of purchase intentions and extends the knowledge about the impact of artificial intelligence in e-retail. Furthermore, this study offers insights into the impact of artificial intelligence in e-commerce-artificial intelligence directly affects purchase intentions and plays an important mediator role in the interaction mechanisms between psychosocial factors and purchase intentions.

Revolutionizing Healthcare and Drug Discovery: The Impact of Artificial Intelligence on Pharmaceutical Development

Artificial intelligence (AI) is reshaping drug discovery and delivery in the pharmaceutical industry, fundamentally transforming traditional methods. In drug discovery, AI algorithms rapidly analyze vast biological and chemical datasets to identify potential drug candidates with unprecedented accuracy. Machine learning models predict compound efficacy and safety, accelerating earlystage drug development. AI also facilitates drug repurposing, uncovering new therapeutic uses for existing medications. At the drug delivery front, AI optimizes formulations and systems, enabling targeted and personalized approaches. Intelligent algorithms enhance the understanding of pharmacokinetics and pharmacodynamics, guiding the development of precision medicine strategies. This integration of AI not only expedites innovative drug discovery but also refines delivery mechanisms, promising more effective and tailored treatments with the potential to revolutionize patient care. The data-processing capabilities of AI drive digitalization and widespread utilization. Applications in drug discovery, development, repurposing, and clinical trials aim to alleviate human workload, expedite objectives, and foster innovation. Despite promising prospects, concerns about job displacement and stringent regulations accompany AI implementation. Emphasizing the intent to augment human labor rather than replace it entirely, the industry anticipates that AI will become a pivotal resource, propelling efficiency, innovation, and advancements in healthcare. This review emphasizes the role of AI in transforming drug discovery and delivery

The Impact of Artificial Intelligence on Corporate Governance

The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitativeliterature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.

Impacts of artificial intelligence on carbon emissions in China: in terms of artificial intelligence type and regional differences

The influence of artificial intelligence (AI) on carbon emissions has long been contentious, and accurately clarifying this impact is crucial for the realization of sustainable smart cities. This article investigates the impact of AI on carbon emissions, distinguishing between applied and innovative AI types. Analyzing data from 267 Chinese cities between 2008 and 2019, the study reveals that applied AI reduces carbon dioxide emissions by an average of 40,100 tons per unit increase. In contrast, innovative AI initially leads to emissions increases before ultimately decreasing, illustrating an inverted U-shaped relationship. Regional variations in AI's impact on emissions are influenced by each region's developmental focus and stage of AI deployment. Specifically, differences arise due to the continuous carbon reduction (-0.024) associated with applied AI and the initial emission increase (0.129) attributed to innovative AI. The study underscores the significance of tailored regional strategies in AI development for fostering sustainable smart cities. These findings contribute to the ongoing discourse on AI's environmental impact, offering insights to inform targeted approaches in green, sustainable urban planning.

The Integration and Impact of Artificial Intelligence in Software Engineering

The Integration and Impact of Artificial Intelligence in Software Engineering

Impact of Artificial Intelligence on Block Model teaching: Opportunities and Challenges from accounting academics’ perspective

Impact of Artificial Intelligence on Block Model teaching: Opportunities and Challenges from accounting academics’ perspective

Boosting Financial Audits with AI: Insights from Auditors at Public Accounting Firms Affiliated with OAI Solusi Manajemen Nusantara

The integration of artificial intelligence (AI) into auditing practices promises to revolutionize the profession by enhancing efficiency, accuracy, and insight. This study investigates AI's impact on 150 auditors from OAI Solusi Manajemen Nusantara member firms across Indonesia, utilizing a Google Form survey. AI automates tasks such as data collection, reconciliation, anomaly detection, and risk assessment, allowing auditors to focus on high-value activities like fraud detection and generating actionable insights. Continuous auditing enabled by AI ensures timely issue detection and resolution, improving transparency and stakeholder communication through more informative audit reports. Challenges include ensuring data quality and understanding AI model outputs, critical for auditors to effectively interpret and trust AI-driven recommendations.

Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence.

This systematic literature review aims to study the role and impact of artificial intelligence (AI) in transforming Ear, Nose, and Throat (ENT) healthcare. It aims to compare and analyse literature that applied AI algorithms for ENT disease prediction and detection based on their effectiveness, methods, dataset, and performance. We have also discussed ENT specialists' challenges and AI's role in solving them. This review also discusses the challenges faced by AI researchers. This systematic review was completed using PRISMA guidelines. Data was extracted from several reputable digital databases, including PubMed, Medline, SpringerLink, Elsevier, Google Scholar, ScienceDirect, and IEEExplore. The search criteria included studies recently published between 2018 and 2024 related to the application of AI for ENT healthcare. After removing duplicate studies and quality assessments, we reviewed eligible articles and responded to the research questions. This review aims to provide a comprehensive overview of the current state of AI applications in ENT healthcare. Among the 3257 unique studies, 27 were selected as primary studies. About 62.5% of the included studies were effective in providing disease predictions. We found that Pretrained DL models are more in application than CNN algorithms when employed for ENT disease predictions. The accuracy of models ranged between 75 and 97%. We also observed the effectiveness of conversational AI models such as ChatGPT in the ENT discipline. The research in AI for ENT is advancing rapidly. Most of the models have achieved accuracy above 90%. However, the lack of good-quality data and data variability limits the overall ability of AI models to perform better for ENT disease prediction. Further research needs to be conducted while considering factors such as external validation and the issue of class imbalance.

Technological innovations in education: a scoping review on the impact of AI on academic integrity

This study presents a systematic review on the impact of artificial intelligence (AI) on academic integrity in higher education. The main objective was to understand how technological innovations, particularly those related to AI, influence the teaching and learning process. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PAGE et al., 2021) guidelines, the methodology involved searching various databases, including IEEE, SAGE, Scopus, ERIC, ACM, Semantic Scholar, and Web of Science. Out of the 3,484 articles initially retrieved, 31 were considered for discussion. The results highlight a growing trend of research regarding the proposed theme with emphasis on ChatGPT and cheating behavior detection systems, demonstrating concern for ethical issues in the academic environment.

Impact of Artificial Intelligence on Marketing – A Conceptual Study

The marketing field has undergone a considerable transformation with the introduction of AI, leading to enhanced performance. The present study is aimed at finding the effect of AI on marketing. This paper includes an in-depth literature review that offers a complete understanding of the application of AI in marketing. Various studies emphasize significant AI applications in marketing, such as neural networks, case-based reasoning, and expert systems, marking a transformative shift from traditional marketing methods. The integration of AI in marketing leverages technologies like natural language processing, machine learning, and sentiment analysis to enhance decision-making processes, providing precise insights into customer lifecycles and market trends. By combining AI with customer and brand experience data, businesses gain a competitive edge and are better equipped to navigate the dynamic marketplace. The paper underscores the key elements of incorporating AI in marketing functions to enhance overall business performance, leading to increased profitability and a competitive advantage. Its objective is to document insights into the AI ecosystem, elucidating how embedded technologies support marketing processes and contribute to organizational success. Furthermore, the paper delves into the synergy between AI and marketing, highlighting their combined potential as the future driving force for successful business organizations.

The Impact of Artificial Intelligence on Financial Markets and Business Operations

The integration of artificial intelligence (AI) in financial markets and business operations has emerged as a transformative force, reshaping traditional practices and unlocking new opportunities. This paper presents a systematic literature review encompassing a wide array of studies on AI applications in finance and business. The review explores AI's role in enhancing financial forecasting, trading strategies, risk management, and fraud detection. It discusses various AI techniques such as machine learning, deep learning, and natural language processing, highlighting their effectiveness in analysing vast datasets and improving decision-making processes. Moreover, the review addresses the implications of AI adoption in optimising business operations, including process automation, predictive analytics, and customer experience enhancement. Key themes include the benefits of AI-driven innovations, such as increased efficiency, cost reduction, and personalised services, alongside challenges related to job displacement, algorithmic bias, and regulatory frameworks. The paper concludes with insights into future research directions aimed at advancing AI's interpretability, transparency, and ethical deployment in financial and business contexts.

The Impact of Artificial Intelligence on the Future of Media

The Impact of Artificial Intelligence on the Future of Media

Research on the Impact of Artificial Intelligence on Corporate Sustainable Development: A Case Study of Alibaba

Research on the Impact of Artificial Intelligence on Corporate Sustainable Development: A Case Study of Alibaba

Training and Verification Requirements for Interpretation of Cardiac CT and MRI: AJR Expert Panel Narrative Review.

The use of cardiac CT and MRI is rapidly expanding based on strong evidence from large international trials. The number of physicians competent to interpret cardiac CT and MRI may be unable to keep pace with the increasing demand. Societies and organizations have prescribed training requirements for interpreting cardiac CT and MRI, with recent updates focusing on the increased breadth of competency that is now required due to ongoing imaging advances. In this AJR Expert Panel Narrative Review, we discuss several aspects of cardiac CT and MRI training, focusing on topics that are uncertain or not addressed in existing society statements and guidelines, including determination of competency in different practice types in real-world settings and the impact of artificial intelligence on training and education. The article is intended to guide updates in professional society training requirements and also inform institutional verification processes.