Resisting the adoption of medical artificial intelligence (AI), it is suggested that this opposition can be overcome by combining AI awareness, AI risks, and responsibility displacement. Through effective integration of public AI dangers and displacement of responsibility, some of these major concerns can be alleviated. The United Kingdom’s National Health Service has adopted the use of chatbots to provide medical advice, whereas heart disease diagnoses can be made by IBM’s Watson. This has the ability to improve healthcare by increasing accuracy, efficiency, and patient outcomes. The resistance may be due to concerns about losing jobs, anxieties about misdiagnosis or medical mistakes, and the consciousness of AI systems drifting more responsibility away from medical professionals. There is hesitancy among healthcare professionals and the general public about the deployment of AI, despite the fact that healthcare is being revolutionised by AI, its uses are pervasive. Participants’ awareness of AI in healthcare, AI risk, resistance to AI, responsibility displacement and ethical considerations were gathered through questionnaires. Descriptive statistics, chi-square tests and correlation analyses were used to establish the relationship between resistance and medical AI. The study’s objective seeks to collect data on primary and public AI awareness, perceptions of risk and feelings of displacement that the professionals have regarding medical AI. Some of these concerns can be resolved when AI awareness is effectively integrated and patients, healthcare providers, as well as the general public are well informed about AI’s potential advantages. Trust is built when, AI related issues such as bias, transparency, and data privacy are critically addressed. Another objective is to develop a seamless integration of risk management, communication and awareness of AI. Lastly to assess how this comprehensive approach has affected hospital settings’ ambitions to use medical AI. Fusing AI awareness, risk management, and effective communication can be used as a comprehensive strategy to address and promote the application of medical AI in hospital settings. An argument made by Chen et al. is that providing training in AI can improve adoption intentions while lowering complexity through the awareness of AI.

The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

This study conducted a systematic literature review on current and emerging trends in the use of artificial intelligence (AI) for community surveillance, using the PRISMA methodology and the paifal.ai tool for the selection and analysis of relevant sources. Five main thematic areas were identified: AI technologies, specific applications, societal impact, regulations and public policy. Our findings revealed exponential growth in the development and implementation of AI technologies, with applications ranging from public safety to environmental monitoring. However, this advancement poses significant challenges related to privacy, ethics and governance, driving a debate on the need for appropriate regulations. The analysis also highlighted the disparity in the adoption of these technologies among different communities, suggesting a need for inclusive policies to ensure equitable benefits. This study contributes to the understanding of the current scenario of AI in community policing, providing a solid foundation for future research and developments in the field.

This research was conducted using a survey research method to investigate the influence of Artificial Intelligence (AI) on Nigerian students' academic performances in tertiary institutions. Nigerian tertiary institutions have an estimated population of about 2.5 million students across the universities, polytechnics, monotechnics, and colleges of education. A sample size of 509 was used. The researchers adopted an online questionnaire (Google Form) to administer questions to respondents across Nigeria to elicit responses from the respondents bordering on their awareness and the use of AI and its attendant impacts on their academic performance. Five research objectives were raised for the proper investigation of this study. From the findings of the study, the researchers found that the majority of Nigerian students use AI and that AI has positive impacts on the educational performance of Nigerian students. It was also found that Nigerian students have training on the use of AI for educational purposes and that they are more familiar with Snapchat AI and ChatGPT. Conclusively, AI is useful to students in the sense that it enhances their knowledge of their courses, improves their learning and speaking skills, and helps them to have a quick understanding of their course by way of simplifying technical aspects of their courses. The researchers therefore recommend as follows: Nigerian tertiary institutions should formally train students as well as teachers on the use of AI for academic purposes so that they can understand the ethical implications of the use of AI. Using AI for writing could be interpreted to mean examination malpractice, and this should not be condoned in the educational sector; however, at the moment, a small number of students used AI for examinations. Albeit, the appropriate use of AI should be fully integrated into Nigerian tertiary institutions' curricula.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

Artificial Intelligence (AI) in education has both positive and negative impacts, particularly in term of increasing plagiarism. This research analyzes Indonesia’s plagiarism regulations and offers solutions. It uses doctrinal methods with legislative, case, and comparative studies, revealing that plagiarism is regulated but not specifically for AI involvement. The results show that plagiarism in scientific work has actually been regulated through several regulations. On the other hand, there is no regulation governing the involvement of AI in the process of preparing scientific articles. Comparative studies show that the US, Singapore, and the EU have advanced regulations for AI in education. The US has copyright laws for AI works and state regulations, Singapore’s Ministry of Education has guidelines for AI integration and ethics, and the EU has the Artificial Intelligence Act. To tackle AI-related plagiarism in Indonesia, the study suggests enacting AI-specific laws and revising existing ones. Ministerial and Rector statutes should address technical aspects of AI use and plagiarism checks. The Ministry should issue guidelines for universities to develop Standard Procedures for Writing and Checking Scientific Work, using reliable AI-checking software. These measures aim to prevent plagiarism in Indonesia’s educational sector.