Digitalization of Environmental Law and Possibilities for Artificial Intelligence Implementation

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

ObjectivesTo explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units...

The research and development situation of environmental governance in the past thirty years may be intuitively acquired with the co-word analysis in the interdisciplinary environmental law. This paper analyzes the research focus and transformation situation of China’s environment governance and law by searching related papers with the subject words “environmental law” in Chinese Journal Full-text Database. The co-word analysis results show environmental law research showed five main knowledge focuses, such as international environmental law, sustainable development, environmental right, participation, and environment conservation. And according to the co-word analysis, environmental law research may be divided into four stages: foundation, prosperity, from theory to practice transformation, and challenge stage of environmental law response to new environment problems. By analysis of the research focus, we can know China’s researchers in the environmental governance policy or law should strengthen the scientific, pertinence and prospective characteristics of environmental law research, make interdisciplinary and comprehensive study combined with China long-term science and technology development.

To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery. We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019-June 2020. We investigated the role and interplay of patient demographic factors, healthcare literacy, understanding of AI, comfort levels with various AI scenarios, and preferences for AI use in clinical care. Of the 349 parturients, 57.6% were between the ages of 25-34years, 90.1% reported college or graduate education and 69.2% believed the benefits of AI use in clinical care outweighed the risks. Cluster analysis revealed 2 distinct groups: patients more comfortable with clinical AI use (Pro-AI) and those who preferred physician presence (AI-Cautious). Pro-AI patients had a higher degree of education, were more knowledgeable about AI use in their daily lives and saw AI use as a significant advancement in medicine. AI-Cautious patients reported a lack of human qualities and low trust in the technology as detriments to AI use. Patient trust and the preservation of the human physician-patient relationship are critical in moving forward with AI implementation in healthcare. Pregnant individuals are cautiously optimistic about AI use in their care. Our findings provide insights into the status of AI use in perinatal care and provide a platform for driving patient-centered innovations.

Artificial intelligence (AI) implementation within the National Health Service (NHS): the South West London AI Working Group experience

The author of the article considers the main directions of application of artificial intelligence in financial management. The article reviews the main scientific sources and positions of practitioners on the strategic prospects of applying artificial intelligence tools in financial management. The research is based on the methodology of systematic review and synthesis of scientific sources and industry reports, which is the most justified for this area of ​​research. The article analyzes key indicators of the artificial intelligence market in the financial sector according to data from leading analytical companies, which demonstrates the impressive growth dynamics of the global artificial intelligence market by 2030-2034. The author systematizes the main challenges faced by the implementation of artificial intelligence in financial management, such as: ethical and regulatory lags, scaling problems, environmental impact and data bias. Based on the analysis of market trends, the evolutionary path of using artificial intelligence tools has been determined, which indicates the growth of market maturity, which is moving from the stage of "technology for the sake of technology" to the stage of "solutions for the sake of business tasks". The author identified strategic vectors for the implementation of artificial intelligence technology in financial management, among which three main ones were conceptually identified: increasing operational efficiency, risk management and cybersecurity, personalization and customer experience. Specific areas of application in which the implementation of artificial intelligence has the most significant results in financial management have also been systematized: fraud detection, creditworthiness assessment, forecasting needs, democratization of financial services, development of virtual assistants, etc. The article pays attention to comparing the main models of generative artificial intelligence and their possibilities of use as tools in financial management, in particular, advantages, limitations and the most optimal type of use.

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Implementation of AI in the Medical Industry in the U.S.

The scientific article examines the prospects of using artificial intelligence in the construction and development of the oil and gas complex of fields. The scientific article describes the implementation and advantages of artificial intelligence (AI) at various stages, namely, the development of oil and gas projects such as geology, drilling, exploitation, production, transportation and processing. This article also discusses possible challenges, risks, and recommendations for the safe use and implementation of AI in the construction and development of the oil and gas complex (NGK). The authors of the article describe in detail and evaluate the directions of application of the AI segment of NGK deposits. Conclusions have been drawn on the implementation of this area, as well as an analysis that can further contribute to the development of the oil and gas complex.

Background: The prospect of Artificial Intelligence (AI) implementation in the healthcare field will revolutionize clinical decision-making and nursing practice. Nevertheless, senior nurses tend to have some reservations toward AI-based Clinical Decision Support Systems (AI-CDSS), especially on the issues of professional autonomy, workload, and even ethics. Purpose: it was to understand the issues and possibilities of introducing AI-based devices to experienced nursing personnel, their attitudes, obstacles, and possible ways of sustainable implementation. Methodology: A narrative review of PubMed, Scopus, CINAHL, and Web of Science (2018–2025) was conducted; eligible full texts were screened and thematically synthesized from 18 included studies. Results: The key obstacles to AI implementation were found to be more cultural and psychological than technical for decision makers. Nurses raised worries regarding the loss of expertise, risk to autonomy, and automation bias. A training co-pilot- a specialist mediating between advanced technology and clinical reality- was needed in the process of effective integration. Strategic imperatives like explainable AI, ethical sensitization, workload alignment, and institutional transparency became essential success factors. Conclusion: The paper highlights how effective AI implementation needs more than just technical implementation; it would need cultural adjustment, institutional openness, and training on specific skills designed to fit experienced personnel. To provide safe and sustainable integration of AI into nursing, it is necessary to create nurse informaticists as co-pilots and introduce ethical, transparent, and explainable practices.

Regulatory and Implementation Considerations for Artificial Intelligence

The rapid development of artificial intelligence (AI) systems has raised concerns about their ethical, legal, and social risks. Despite notable progress in the development of responsible AI frameworks, methods, and tools, research shows that many organizations struggle to effectively implement responsible AI. I review prior research on responsible AI to explain the insufficient implementation of responsible AI in organizations. Drawing on neo-institutional theory, I find that policy-practice decoupling (i.e., organizational responsible AI policies are adopted but not implemented in practice) and means-end decoupling (i.e., organizational responsible AI policies are implemented in practice but do not achieve their intended goals) can explain the ineffective implementation of responsible AI, with AI practitioners playing a key role as institutional entrepreneurs or custodians in driving or inhibiting the implementation of responsible AI. I contribute to the literature on responsible AI by exploring the institutional pressures that drive or inhibit its implementation, synthesizing the challenges to its implementation, and providing an overview of the roles and strategies AI practitioners use to deal with the implementation of responsible AI. I propose several avenues for future research and discuss implications for research and practice.

Purpose: This research aims to identify the impact of Artificial Intelligence (AI) implementation on three core Human Resource Management (HRM) functions: recruitment, training and development, and talent management.Additionally, it seeks to determine the extent to which AI implementation serves as a catalyst for enhancing the efficiency, strategic alignment, and overall effectiveness of HR practices within modern organizational contexts.Design/Methodology/Approach: A hypothetical-deductive research design was employed. A conceptual model was developed from a synthesis of extant literature, positing three direct-effect hypotheses. The questionnaire was distributed to 152 participants randomly at- japan food solutions- private sector company. The measurement model was validated for reliability, convergent validity, and discriminant validity. The hypotheses were tested using variance-based Partial Least Squares Structural Equation Modeling .Findings: The structural model analysis provides robust empirical evidence supporting all hypotheses. AI implementation demonstrates a statistically significant and strong positive effect on the recruitment process (β = 0.778, p < 0.001), the training process (β = 0.702, p < 0.001), and the talent management process (β = 0.701, p < 0.001). The model explains substantial variance in these endogenous constructs (R² = 0.605, 0.493, and 0.492, respectively), confirming AI's potent role as a trans-formative agent in HRM.

The implementation of Artificial Intelligence (AI) into transportation systems signifies a fundamental change that holds the potential to improve overall sustainability, safety, and efficiency. However, this transformation is not without its challenges, particularly concerning human factors and ergonomics. Firstly, the ergonomic integration of AI systems in transportation requires careful consideration of human-AI interaction. Unlike traditional systems, AI-driven technologies often involve complex algorithms that are not inherently intuitive to human operators. This complexity can lead to a disconnect between how the system operates and how users understand it. For instance, in autonomous vehicles, drivers may be required to take control in certain scenarios. The ergonomic challenge here lies in ensuring that the transition between AI and human control is seamless and that the user is adequately informed and prepared to take over. Another significant challenge is addressing the variability in human behavior and expectations. AI systems, designed based on standard models of behavior, may not adequately account for the wide range of human responses and interactions. This gap can lead to scenarios where AI systems behave in unexpected or counterintuitive ways to human users, potentially causing confusion and reducing the overall efficiency and safety of the transportation system. CAE Advanced Air Mobility (AAM) research case study focuses on the implementation and certification of AAM in the FAA/EASA environment.The reliability and trustworthiness of AI systems also pose a major ergonomic challenge. To fully integrate AI into transportation, users must trust these systems. This trust depends on the transparency and explainability of AI decision-making processes. Nevertheless, numerous AI algorithms, especially those rooted in deep learning, are frequently regarded as 'black boxes' owing to their intricacy and absence of interpretability. Developing AI systems that are both advanced and transparent is a significant hurdle that needs to be addressed.Data privacy and security are also paramount concerns. AI systems in transportation rely heavily on data, including personal and sensitive information. Ensuring the privacy and security of this data while utilizing it for AI processes is a complex challenge, requiring robust encryption methods and strict data handling policies. Moreover, the ergonomic aspect involves designing systems that not only protect data but also respect user privacy preferences and norms.The implementation of AI in transportation is a multifaceted challenge, requiring a holistic approach that considers human factors and ergonomics. As we move towards more AI-integrated transportation systems, it is essential to address these challenges through interdisciplinary research, collaboration, and a user-centered design approach.

BackgroundThe purpose of this paper is to develop and validate a standardized endoscopist acceptance scale for the implementation of artificial intelligence (AI) in gastrointestinal endoscopy.MethodsAfter investigating endoscopists who have previously used AI and consulting with AI experts, we developed a provisional scale to measure the acceptance of AI as used in gastrointestinal endoscopy that was then distributed to a sample of endoscopists who have used AI. After analyzing the feedback data collected on the provisional scale, we developed a new formal scale with four factors. Cronbach's alpha, confirmatory factor analysis (CFA), content validity, and related validity were conducted to test the reliability and validity of the formal scale. We also constructed a receiver operating characteristic (ROC) curve in order to determine the scale's ability to distinguish higher acceptance and satisfaction.ResultsA total of 210 valid formal scale data points were collected. The overall Cronbach's alpha was 0.904. All the factor loadings were >0.50, of which the highest factor loading was 0.86 and the lowest was 0.54 (AVE = 0.580, CR = 0.953). The correlation coefficient between the total score of the scale and the satisfaction score was 0.876, and the area under the ROC curve was 0.949 ± 0.031. Endoscopists with a score higher than 50 tend to be accepting and satisfied with AI.ConclusionThis study yielded a viable questionnaire to measure the acceptance among endoscopists of the implementation of AI in gastroenterology.