The Technological Evolution, Applications and Future Prospects of Representative Artificial Intelligence Models

The integration of AI is anticipated to bring a major transformation to the quality, value, and impact of radiology on patient care and population health, as well as radiologists' workflows, over the next decade.AI technologies are already being applied across various dental specialties, including orthodontics.There are various applications of AI in Oral and Maxillofacial Radiology.AI is expected to ease the daily workload of physicians and reduce the incidence of misdiagnosis or underdiagnosis in dental practice.Although AI systems are a valuable resource in both dentistry and dental education, it's crucial to recognize that these technological advancements stem from human innovation and discovery.The ability of a machine to mimic intelligent human behaviors to perform complicated tasks like problem-solving, object and word recognition, and decision-making is known as artificial intelligence or AI [1,2].AI has gained popularity recently in several areas, including dentistry, where its uses in oral and maxillofacial imaging show significant promise.Because radiology creates digitally coded images, integrating AI into medical procedures is much simpler [3].Convolutional neural networks, which are capable of handling tasks including image categorization, detection, segmentation, registration, creation, and refinement, are the main focus of recent studies in oral and maxillofacial radiology [4].In this field, artificial intelligence systems have been developed for image analysis, forensic dentistry, radiography diagnosis, and picture quality enhancement [4].However, obtaining useful results necessitates large data sets, and oral and maxillofacial radiologists play a critical role in creating reliable and consistent datasets, which can take a lot of time.The use of AI is expected to bring about a dramatic revolution in the next ten years regarding the workflows of radiologists and the impact of radiology on population health and patient care.Dentists need to use their skills to evaluate patients, choose the best course of treatment, and make clinically sound prognostic judgments.They might, however, occasionally lack the expertise needed to act swiftly and decisively in clinical decisions.Applications of AI can be useful tools for improving performance and decision-making.AI is already being used in several dental specializations, such as orthodontics.The majority of current research focuses on artificial intelligence (AI) models that use artificial neural networks (ANNs) and convolutional neural networks (CNNs) [5].AI developments are anticipated to lessen the everyday burden on healthcare professionals and lower the number of dental misdiagnoses and underdiagnoses.Radiological image research encompasses tasks such as segmenting and classifying teeth, automatically labeling anatomical structures, performing cephalometric analysis, detecting early dental diseases and periodontal conditions, and using jaw radiographs to diagnose specific diseases like osteoporosis [6].

Since 2019, artificial intelligence (AI) has played an increasingly central role in daily life. Yet, this phase in AI’s evolution is only the beginning of even greater advancements. Catholic institutions, cognizant of the crucial role the Church must assume in the development of AI, have increasingly engaged with this technological frontier. Among these institutions is Longbeard, which has undertaken the creation of Magisterium AI, a compound AI system that leverages several AI technologies and a large database of Catholic knowledge to produce faithful answers. This article offers a critical analysis of the current state of Magisterium AI, drawing on its 2025 Report, Investor Deck, user feedback, and direct engagement with the system to evaluate its alignment with Catholic ethical principles. Specifically, it highlights the expansion of the database as a key area for improvement, while reflecting on the complexities inherent in such an undertaking. The article concludes that the development of a Catholic AI entails more than simply amassing documents in a database. It requires endowing the AI system with a profound grasp of Scripture and its interpretive traditions; a comprehensive knowledge of theology as a whole; a broad acquaintance with the history of ideas, including the development of culture, art, philosophy, and theology; and a finely honed awareness of the evolving nuances of language and cultural sensibilities through which eternal truths are expressed.

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

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.

Purpose This study analyzes China’s strategic initiatives in metaverse and artificial intelligence (AI) development, examining their impact on academic research, industry innovation, and policy formulation. It aims to understand how government policies and investments have shaped research agendas and to identify challenges and opportunities in these fields. Design/methodology/approach The research employs a comprehensive analysis of government documents, funding schemes, and research output. It examines key policies, investment programs, and academic publications to track trends in metaverse and AI development in China. The study utilizes bibliometric analysis to assess publication trends, citation patterns, and international collaboration networks. Findings China’s proactive approach, characterized by strong government support and significant private sector investment, has led to a substantial increase in research output and quality in metaverse and AI fields. Chinese institutions have become major contributors to global publications, with growing citation rates and presence at international conferences. The research identifies emerging challenges in privacy, ethical AI development, and digital divide concerns. Practical implications The findings provide insights for policymakers, researchers, and industry stakeholders on the development trajectory of metaverse and AI technologies in China. They highlight the need for balanced approaches to innovation, regulation, and ethical considerations in these rapidly evolving fields. Social implications The study underscores the potential of metaverse and AI technologies to transform various sectors of society, from education and healthcare to entertainment and social interactions. It emphasizes the importance of addressing digital equity and ethical AI deployment to ensure broad societal benefits. Originality/value This research offers a comprehensive overview of China’s approach to metaverse and AI development, providing a unique perspective on the interplay between government initiatives, academic research, and industry innovation. It contributes to the broader discussion on the global development of these transformative technologies and their implications for future technological landscapes.

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

The quest to develop Artificial Intelligence (AI) systems that possess human-like consciousness necessitates a deep dive into both theoretical and practical aspects underpinning this ambitious goal. This article builds on initial philosophical explorations of AI consciousness by examining the intricate and often hidden structures that may facilitate conscious experiences in AI. Drawing from concepts in cognitive science and neuroscience, the article elucidates how AI systems can be designed to replicate the structural and functional aspects of human consciousness. The discussion includes the Hierarchy of Spatial Belongings proposed by Forti (2024), frameworks like the Integrated Information Theory (IIT), and models linking consciousness with metacognitive processes. Through case studies of advanced AI systems such as IBM Watson, AlphaGo, GPT-3, and Sophia the Robot, the article explores practical implementations and their alignment with theoretical models of consciousness. The potential for AI to achieve states analogous to human consciousness raises profound ethical, societal, and legal considerations. Ethical guidelines and legal frameworks are urgently needed to address the moral status and rights of conscious AI systems, ensure their ethical treatment, and delineate accountability. The societal impacts of conscious AI, including job displacement and the need for equitable access to AI technologies, are also examined. Future research directions highlight the necessity for developing sophisticated theoretical models, enhancing practical implementations, establishing comprehensive ethical frameworks, fostering interdisciplinary collaboration, and engaging the public. By addressing these areas, the scientific community can significantly advance the development of conscious AI, ensuring it is both technically feasible and ethically sound

Artificial intelligence technologies are developing intensively today, including due to the development of technologies of stable neural networks and cloud computing infrastructures, fuzzy system technologies, entropy administration, swarm intelligence, evolutionary computations, and many others. etc. At the same time, the problem of almost complete absence of normative legal regulation and normative technical regulation of the foundations, conditions and features of development, functioning and activities, integration into other systems and control over the use of artificial intelligence technologies is global today. The Article is devoted to the study of the specifics of the legal regulation of the use and development of artificial intelligence. Some approaches to the definition of artificial intelligence and the features of legal regulation of this sphere, which take place in the scientific literature, are considered and the author's definition of artificial intelligence is developed and given through the disclosure of its main features. In particular, according to the proposed definition, artificial intelligence is an artificial complex cybernetic computer-software-hardware system possessing the properties of substantivity, autonomy, and also the ability to perceive and analyze data, to self-learn. The question of positioning the artificial intelligence system as a special form of person (for example, the so-called «electronic person»), that is, granting it a certain legal personality depending on various factors and the field of application of such a system, is considered in this article. The article also notes the main possible approaches to legal support for the use and development of artificial intelligence systems, in particular, such approaches include the preemptive universal-total legal regulation and legal regulation aimed at the regulation of specific artificial intelligence systems. The main risks and uncertainties associated with artificial intelligence (and which are of great importance for the adoption of legislation in this field) have been investigated. There are drawn the conclusions about how to develop legislation on the use and development of artificial intelligence: consistently, taking into account the specifics of areas of its application, as well as balancing the interests of individuals, society and the state with regard to proper security and protection of individual rights, and interests related to the development of innovation for the benefit of the whole society.

The primary focus of this article is a general discussion of the theory and development of artificial intelligence (AI) systems and their expanding implications for human interactions and socioeconomic issues. We provide an overview of AI origins and the ubiquitous forms of AI that now saturate our culture, and we discuss the many ways AI is likely to continue to have a pervasive influence on every aspect of our lives. In a separate but directly related article in this issue, we provide an experimental analysis of a current version of an AI in the form of a deep neural network, and we describe how it is able to model and forecast human learning. Throughout this article, we attempt to link common elements in both articles. However, in this article, the emphasis is on a comparison of how cloud-based AI systems impact U.S. citizens and citizens of the European Union in terms of individual rights, security, equitable access, inadvertent machine discrimination, and corporate responsibilities. We note that although AI offers researchers throughout academic disciplines fascinating and previously unimagined explorations and critically important research possibilities, there are areas in which AI systems may be unscrupulously misused.

We are only at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. The recent developments in AI are the result of increased processing power, improvements in algorithms and the exponential growth in the volume and variety of digital data. Many applications of AI have started entering into our every-day lives, from machine translations, to image recognition, and music generation, and are increasingly deployed in industry, government, and commerce. Connected and autonomous vehicles, and AI-supported medical diagnostics are areas of application that will soon be commonplace. There is strong global competition on AI among the US, China, and Europe. The US leads for now but China is catching up fast and aims to lead by 2030. For the EU, it is not so much a question of winning or losing a race but of finding the way of embracing the opportunities offered by AI in a way that is human-centred, ethical, secure, and true to our core values. The EU Member States and the European Commission are developing coordinated national and European strategies, recognising that only together we can succeed. We can build on our areas of strength including excellent research, leadership in some industrial sectors like automotive and robotics, a solid legal and regulatory framework, and very rich cultural diversity also at regional and sub-regional levels. It is generally recognised that AI can flourish only if supported by a robust computing infrastructure and good quality data: • With respect to computing, we identified a window of opportunity for Europe to invest in the emerging new paradigm of computing distributed towards the edges of the network, in addition to centralised facilities. This will support also the future deployment of 5G and the Internet of Things. • With respect to data, we argue in favour of learning from successful Internet companies, opening access to data and developing interactivity with the users rather than just broadcasting data. In this way, we can develop ecosystems of public administrations, firms, and civil society enriching the data to make it fit for AI applications responding to European needs. We should embrace the opportunities afforded by AI but not uncritically. The black box characteristics of most leading AI techniques make them opaque even to specialists. AI systems are currently limited to narrow and well-defined tasks, and their technologies inherit imperfections from their human creators, such as the well-recognised bias effect present in data. We should challenge the shortcomings of AI and work towards strong evaluation strategies, transparent and reliable systems, and good human-AI interactions. Ethical and secure-by-design algorithms are crucial to build trust in this disruptive technology, but we also need a broader engagement of civil society on the values to be embedded in AI and the directions for future development. This social engagement should be part of the effort to strengthen our resilience at all levels from local, to national and European, across institutions, industry and civil society. Developing local ecosystems of skills, computing, data, and applications can foster the engagement of local communities, respond to their needs, harness local creativity and knowledge, and build a human-centred, diverse, and socially driven AI. We still know very little about how AI will impact the way we think, make decisions, relate to each other, and how it will affect our jobs. This uncertainty can be a source of concern but is also a sign of opportunity. The future is not yet written. We can shape it based on our collective vision of what future we would like to have. But we need to act together and act fast.

Recent developments in artificial intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy and how AI systems work. However, increased transparency could lead to negative outcomes if humans become overreliant on the technology. This study investigated how changes in AI transparency affected human decision-making in a medical-screening visual search task. Transparency was manipulated by either giving or withholding knowledge about the accuracy of an ‘AI system’. We tested performance in seven simulated lab mammography tasks, in which observers searched for a cancer which could be correctly or incorrectly flagged by computer-aided detection (CAD) ‘AI prompts’. Across tasks, the CAD systems varied in accuracy. In the ‘transparent’ condition, participants were told the accuracy of the CAD system, in the ‘not transparent’ condition, they were not. The results showed that increasing CAD transparency impaired task performance, producing an increase in false alarms, decreased sensitivity, an increase in recall rate, and a decrease in positive predictive value. Along with increasing investment in AI, this research shows that it is important to investigate how transparency of AI systems affect human decision-making. Increased transparency may lead to overtrust in AI systems, which can impact clinical outcomes.

Recent developments in Artificial Intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy and how AI systems work. However, increased transparency could lead to negative outcomes if humans become over-reliant on the technology. This study investigated how changes in AI transparency affected human decision making in a medical screening visual search task. Transparency was manipulated by either giving or withholding knowledge about the accuracy of an ‘AI system’. We tested performance in seven simulated lab mammography tasks, in which observers searched for a cancer which could be correctly or incorrectly flagged by Computer Aided Detection (CAD) ‘AI prompts’. Across tasks the CAD systems varied in accuracy. In the ‘Transparent’ conditions participants were told the accuracy of the CAD system, in the ‘Not Transparent’ they were not. The results showed that increasing CAD transparency impaired task performance producing an increase in false alarms, decreased sensitivity, an increase in Recall Rate and a decrease in Positive Predictive Value. Along with increasing investment in AI this research shows that it is important to investigate how transparency of AI systems affect human decision making. Increased transparency may lead to over-trust in AI systems, which can impact clinical outcomes.

Artificial Intelligence is a science that aiming to creating systems to imitate human wisdom, and perform tasks such as learning, reasoning, problem-solving, perception, natural language understanding, and some forms of creativity. This paper is about handwritten number classifier, a specific field within artificial intelligence, and its use across various industries. The basic concepts about artificial intelligence will be given at the first and the definition and structure of three representative artificial intelligence algorithms, convolutional neural networks, recurrent neural networks, and long short-term memory neural networks will be exemplified to better illustrate the concepts. Furthermore, the applications of handwritten number classifier will be analysed, especially in the areas of large-scale data statistics or survey, finance and taxation, and mail sorting. Eventually, a conclusion encompassing the challenges that the artificial intelligence systems are faced and reasoning regarding the significant role that artificial intelligence plays in the urban areas of the world is given for further discussion.

This article dives into the ethical complexities of Artificial Intelligence (AI). It starts by laying the groundwork, defining both ethics and morality, and emphasizing how ethical principles can evolve over time.Next, it explores "applied ethics," which examines real-world dilemmas like genetic engineering, AI's impact on jobs, and environmental concerns. From there, it shifts its focus specifically to AI ethics, differentiating between two key aspects the Ethics of AI where broader framework considers ethical issues surrounding AI development, encompassing concerns like transparency, data security, privacy, and accountability and in Ethical AI where the concept focuses on building AI systems with the ability to make ethical choices. It emphasizes aligning these systems with ethical values, minimizing bias, and fostering user trust. The article delves deeper into specific ethical concerns in the AI landscape, such as transparency, data privacy, and issues of autonomy, intentionality, and responsibility. It explores their relevance and the challenges they pose for AI development. Furthermore, it examines the social implications of these ethical concerns. Key areas examined include automation and job displacement, equitable access to technology, and the potential impact of AI on democratic principles and civil liberties. The article highlights the potential consequences of AI-driven automation on employment, the need for inclusive digital access, and the importance of ethical considerations in governance and citizen rights amidst the growing influence of AI. The article serves as a comprehensive guide to understanding the ethical dimensions of AI. It covers fundamental ethical concepts, specific AI ethics considerations, the importance of building ethical AI systems, and the challenges and societal impacts of AI development. It's a valuable resource for anyone seeking to understand the ethical landscape surrounding Artificial Intelligence.