ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В РАБОТЕ ВРАЧА-СТОМАТОЛОГА. АКТУАЛЬНОСТЬ. ОБЗОР. РИСКИ

Purpose While the literature on artificial intelligence (AI) capability is expanding, gaps remain in understanding how this capability is internally developed in technology-based startups (TBS) across different life cycle phases. This study, grounded in the resource orchestration theory (ROT), investigates the pathway through which TBS use organizational creativity to build AI capability and achieve performance. Design/methodology/approach A conceptual framework based on ROT emphasizes the role of organizational creativity in the structuring and bundling processes. Data were collected through a survey of 166 managers and employees of TBS operating in Brazil and international markets, using multiple linear regressions and the Sobel test for analysis. The study validated the AI capability scale in the TBS context. Findings AI capability fully mediates the relationship between organizational creativity and performance, confirming that organizational creativity is a critical resource for AI capability development. These findings advance ROT by deepening the understanding of how AI capability is developed in TBS. The study offers a dynamic, process-based view of performance trajectories in TBS, demonstrating that the synchrony between creativity and AI capability creates a cyclical process, maximizing company performance. Originality/value This research identifies an alternative pathway for TBS to develop AI capability and achieve performance, highlighting the synchronization and co-evolution of resources and capabilities. It provides novel insights into AI capability’s mediating role and expands understanding of resource management in TBS across life cycle phases.

Purpose This study aims to examine how big data analytical intelligence (BDAI) assimilation promotes new product performance (NPP) in business-to-business (B2B) manufacturing firms. This study tests the intermediary role of artificial intelligence (AI) capabilities and the contingency impact of electronic supply chain collaboration (ESCC) in the relationship between BDAI assimilation and NPP. Design/methodology/approach Drawing on the dynamic capabilities theory (DCT), this study tests the moderated-mediation model using multi-wave, multi-source data collected from 291 Chinese B2B manufacturing firms. Structural equation modeling was applied to test the proposed hypotheses. Findings The results demonstrate that BDAI assimilation significantly promotes NPP, and AI capabilities act as an intermediary bridge – empowering B2B firms to convert BDAI assimilation into enhanced NPP. This study further found that this mediation model is strengthened through the contingency impact of ESCC and increases its indirect effect on NPP. Practical implications This study suggests that B2B managers and policy architects should recognize that investment in BDAI assimilation is not sufficient. However, building AI capabilities might fully support BDAI assimilation to gain innovation outcomes such as NPP. This study further suggests the practical implications of ESCC in achieving higher returns through AI capabilities in the B2B context. Originality/value This study has threefold contributions to the existing literature on big data innovation and B2B firms. This study contributes to extend DCT by emphasizing AI capabilities as an intermediary channel and ESCC as a vital contingency – strengthening the relationship between BDAI assimilation and NPP. We contributed and understand how Chinese B2B firms strategically used big data and AI strategies to reach firms’ competitive advantage.

In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.

Sustainable development with Artificial Intelligence: Examining the absorptive capacity pathways to green innovation.

Incumbent industrial firms are putting in a lot of effort in developing capabilities for machine learning (ML) systems that help them better predict and perform a variety of industrial and business processes and decisions. Given the data-, process-, and organizational structure-related requirements for effective implementation of such systems, these organizations encounter a major challenge in developing capabilities in this context. However, the existing literature has yet to unravel the organizational processes and practices associated with artificial intelligence (AI) capability development and deployment in industrial incumbent firms. The present study frames AI adoption in established industrial firms as a process of history-embedded, situated organizational learning involving explorative and exploitative learning. Based on a qualitative study of seven firms utilizing ML algorithms in their industrial and business processes, we develop a grounded model that explains AI capability building as both enabled and constrained by perceptual and functional triggers and barriers, leveraged via communicative and structural practices, and resulting in ongoing and interdependent processes of exploration and exploitation. The study contributes to the literature by showing how the convergence of organizational learning and AI technology's unique features promotes a distinct dynamic of AI capability building and deployment.

Purpose Artificial intelligence (AI) is a significant trend in digital technology that is revolutionizing the field of global business and internationalization. Based on institutional theory and resource-based view, this study examines the intricate relationship between AI capabilities and export performance, taking into account the different province market development within a country and cultural distance. Design/methodology/approach This study gathered data from the websites of privately owned Chinese exporters and complemented it with a survey in 2023. In conducting the survey, we employed a simple random-sampling approach to select 1,500 exporters in China, with a focus on economic development indicators, particularly GDP contribution. We received 1,000 surveys, but only 749 were valid due to missing data. The study’s comprehensive coverage of regions ensured the inclusion of potential variations and subnational disparities within the country. This study conducted ordinary least squares (OLS) regression, and standardized variables before entering into the regressions. Findings The results demonstrate that AI capabilities have a significant positive impact on export performance. In addition, the influence of AI capabilities on export performance varies depending on the home-country and host-country institutional environment. The relationship between AI capabilities and export performance is strengthened by larger cultural distance, while province market development within a country has a negative moderating effect on this relationship. In less developed markets, the AI capabilities can drive significantly export performance. In developed markets with more advanced institutional development, the significance of AI capabilities in reducing transaction costs diminishes due to established institutions and market structures. AI capabilities serve as an intermediary institutional mechanism that connects the institutional context of the home country with the cultural environment of the host country. Originality/value While the impact of AI on international business and internationalization performance is a growing area of study, further exploration of the moderating factors that influence this relationship is needed. Organizations operating in diverse global markets are profoundly shaped by institutional contexts in their operational environments. This research addresses the relatively unexplored role of institutional factors within the home country (provincial market development) and host country (cultural distance) in moderating the effects of AI capabilities on export performance. This study illuminates the intricate dynamics underlying the relationship between AI capabilities and export performance, with a specific focus on province-level market development and cultural distance. Employing institutional theory as the overarching framework, this research sheds light on how AI serves as an intermediary institutional mechanism, bridging the gaps related to cultural differences and varying market development levels. In doing so, it contributes to academia by advancing our understanding of how AI is shaping internationalization dynamics and the interaction between AI capabilities and institutional factors. Additionally, it offers insights for business managers and policymakers to make informed strategic decisions regarding AI capabilities.

Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies.

PurposeThis paper presents pyBibX, a Python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Scopus, Web of Science and PubMed, seamlessly integrating state-of-the-art artificial intelligence (AI) capabilities into its core functionality.Design/methodology/approachThe library executes a comprehensive exploratory data analysis (EDA), presenting outcomes via visually appealing graphical illustrations. Network capabilities have been deftly integrated, encompassing citation, collaboration and similarity analysis. Furthermore, the library incorporates AI capabilities, including embedding vectors, topic modeling, text summarization and other general natural language processing tasks, employing models such as sentence-BERT, BerTopic, BERT, chatGPT and PEGASUS.FindingsAs a demonstration, we have analyzed 184 documents associated with “multiple-criteria decision analysis” published between 1984 and 2023. The EDA emphasized a growing fascination with decision-making and fuzzy logic methodologies. Next, network analysis further accentuated the significance of central authors and intra-continental collaboration, identifying Canada and China as crucial collaboration hubs. Finally, AI analysis distinguished two primary topics and chatGPT’s preeminence in text summarization. It also proved to be an indispensable instrument for interpreting results, as our library enables researchers to pose inquiries to chatGPT regarding bibliometric outcomes. Even so, data homogeneity remains a daunting challenge due to database inconsistencies.Originality/valuePyBibX is the first application integrating cutting-edge AI capabilities for analyzing scientific publications, enabling researchers to examine and interpret these outcomes more effectively. pyBibX is freely available at https://bit.ly/442wD5z.

In order to solve the problem of enterprises lacking the ability to quickly adapt to complex dynamic environments, the ability to create value, and the ability to continuously obtain competitive advantages, the author proposes the impact of enterprise artificial intelligence capabilities on R&D leaps.Based on a sample of Chinese manufacturing listed companies from 2012 to 2021, the author used machine learning methods to measure the artificial intelligence capabilities of enterprises, and empirically analyzed the impact of enterprise artificial intelligence capabilities on R&D leaps from the perspective of human-machine collaboration.The results showed that using the Utest test, the calculated extreme points were 15.48, 1.98, and 0.51, all of which were within the range of data values and could reject the original hypothesis at a statistical level of 6%.Through empirical research, it has been found that there is an inverted Ushaped relationship between the three dimensions of artificial intelligence capabilities and R&D leaps behavior, where too much is too little.The order of impact from high to low is artificial intelligence management ability, artificial intelligence analysis ability, and artificial intelligence basic resource ability; The dynamic nature of the environment enhances the relationship between various capabilities of enterprise artificial intelligence and R&D leaps by influencing human-machine collaboration.Conclusion: The author's research expands the measurement methods and research boundaries of enterprise artificial intelligence capabilities, and also provides some inspiration and reference for enterprises to grasp the pace of R&D leaps and seize technological opportunities.

With the continuous development and widespread adoption of artificial intelligence (AI) technology, enterprises have realized the importance of possessing artificial intelligence capabilities to enhance organizational performance. However, there is still a relative lack of empirical research on how to build AI capabilities, and how AI capabilities impact organizational innovation performance. Based on the resource-based theory, this study constructs a conceptual model of “AI Capabilities - Organizational Response (Organizational Learning and Organizational Agility) - Ambidextrous Innovation”, exploring the direct impact of AI capabilities on ambidextrous innovation, and the mediating effects of organizational learning and organizational agility, also the moderating role of digital strategy in this linkage. With data from 253 survey questionnaires of Chinese enterprises, the results indicate that: first, AI capabilities have a significantly positive impact on organizational ambidextrous innovation; second, organizations with high levels of organizational learning ability and agility can better leverage AI capabilities to promote the development of ambidextrous innovation; Thirdly, digital strategy has played an active role in regulating the influence path of two-way innovation within the organization and provided sufficient boundary conditions; fourth, The data capability, technological capability, and fundamental resource capability of AI capabilities have differentiating effects on exploratory and exploitative innovations. The findings of this research hold significant theoretical and managerial value for understanding the driving mechanisms of organizational innovation performance in the era of AI.

Artificial intelligence is one of the leading technologies today, which is rapidly spreading into all spheres of human activities including education. In 2023, GPT chat was ranked fourth in the Top 100 Tools for Learning rating, which demonstrates the powerful potential of artificial intelligence tools for the development of innovative processes in education. Accordingly, educators need to understand what artificial intelligence is, its features, and how it can be used to replace the routine activities of both teachers and students in teaching and learning. The authors conducted a survey of primary school teachers about their understanding of this notion, ways to use it as their assistant, advantages and disadvantages of artificial intelligence, and prospects for its development and application in all areas of human activity. The level of respondents' knowledge and skills in artificial intelligence, its features and functions was determined. The professional standard of a primary school teacher and the types of activities, through which the teacher's labor functions are realized and for which it is advisable to use artificial intelligence tools, are analyzed. The development of primary school teachers' competencies can be positively influenced by the use of artificial intelligence in their professional activities. The authors describe digital artificial intelligence tools that will be useful for primary school teachers and classify them according to the types of their pedagogical activities. The areas of activity in which artificial intelligence can act as a teacher's assistant, as well as areas that are currently beyond the capabilities of artificial intelligence, are identified. It is concluded that primary school teachers need to be trained to use artificial intelligence in their professional activities and to transfer knowledge about artificial intelligence to pupils, as well as ways to teach pupils the features of artificial intelligence, methods of its application for teaching pupils and for their own professional development.

As the Industry 4.0 paradigm accelerates, the importance of artificial intelligence (AI) in manufacturing industry is increasing. Manufacturing AI requires balanced capabilities between industry-specific domain knowledge and AI capability. Nevertheless, many manufacturing AI startups are mainly focusing on human capital based on AI or data science capability. This article focused on the importance of human capital based on domain knowledge for the success of manufacturing AI. In this article, the relationship between domain knowledge and corporate performance was analyzed for 127 global manufacturing AI startups. Furthermore, the moderating effects of educational level and cofounder size for domain knowledge were analyzed. In addition, an expanded analysis was conducted on effect between domain knowledge, educational level, cofounder size, and corporate performance by business model. This article has new implications for and provides practical contributions to the human capital strategy for manufacturing AI corporates.

Ідея використання можливостей штучного інтелекту для потреб навчального процесу не нова для сучасної педагогіки, але її розвиток до початку пандемії COVID-19 був досить фрагментованим. Пандемія, самоізоляція та тривалий карантин змусили шукати нові можливості та форми для відтворення навчального процесу в цій ситуації. І тут на перший план вийшли можливості організації навчального процесу для всіх рівнів освіти з використанням штучного інтелекту. Мета цієї статті - проаналізувати форми використання можливостей штучного інтелекту для організації навчального процесу для учнів початкових класів, що активізувалися через пандемію COVID-19. Використання авторських методів особистого навчання (UPTM) під час навчання в школі завжди вважалося ознакою ідентичності та елітарності навчального процесу в конкретній країні. UPTM, з одного боку, більше відповідає потребам країни та суспільства у підготовці молодого покоління. З іншого боку - це можливість вузької конкретизації навчального процесу. У процесі написання статті були використані емпіричні та теоретичні методи дослідження, характерні для наукових досліджень такого роду. В результаті можна зазначити, що карантин, спричинений пандемією COVID-19, зіграв сприятливу роль у розширенні спектру можливостей використання штучного інтелекту в галузі освіти, навіть у тих сферах, які традиційно вважалися непридатними або неефективними для їх використання через особливості навчальних завдань. Статтю опубліковано в рамках дослідницької програми «Порівняльне дослідження професійного розвитку вчителів початкових класів у Китаї та Південній Кореї» (реєстраційний номер ZKNUC2017038)

The capabilities of artificial intelligence (AI) in medicine are expanding at an unprecedented pace. AI has the potential to serve as an invaluable asset in harnessing large amounts of data to generate new diagnostic models, inform clinical decision making, and to expand the capabilities of modern medicine. AI has proven beneficial in various fields of diagnostic medicine including radiology, pathology, and laboratory medicine. AI programs have been shown to increase workflow without compromising accurate identification of abnormalities. Aspects of human error in medicine can be mitigated through the use of machine learning programs to serve as quality assurance and failsafe strategy. The advent of AI in healthcare carries many ethical, legal, and accountability challenges. These challenges include issues of transparency, data security, informed consent, and liability. Lack of familiarity with AI systems and fear of unforeseen consequences warrants caution in the implementation of these new tools. However, undue delays in the implementation of AI may unnecessarily hinder the progress of medicine and limit the level of care that can be provided to patients. Healthcare providers should ensure they are well-positioned to adapt to this new technological era of medicine by remaining up to date on the implications of technological advances. Medical education will require continuous revaluation and adaptation to ensure learners gain the appropriate digital literacy to function effectively in AI-assisted medical practice. The implementation of these technologies into healthcare systems represents the greatest healthcare transition of our time. Optimizing this transition in a growing number of medical disciplines will require tactful implementation and diligent risk management.KeywordsQuality improvementArtificial intelligenceHealthcare deliveryMachine learningHealthcare optimization

The article identifies current trends in mosaic art and shows that the popularity of mosaic is due to its versatility, aesthetic qualities and the ability to create unique and individual designs. The authors presented the directions of using mosaic in modern interiors and described the capabilities of artificial intelligence in creating mosaic panels. The article proposes a new approach that uses a multi-tasking planning algorithm to combine graphic design tasks with digital art. Mosaic remains very popular in modern interiors, although its use has evolved over time. It adds texture, color, pattern and an element of handwork, which is valued in modern design. The popularity of mosaic in modern interiors is due to its characteristics such as versatility, variability of materials, the ability to create unique designs, and functionality. The authors suggest using ChatGPT or Copilot artificial intelligence to design interiors with mosaics. The advantage of this approach is the ability to generate a significant number of images of interiors in different styles and with instant consideration of the customer’s wishes. The use of artificial intelligence by a designer to model mosaic panels in different styles can significantly speed up the designer’s work and make the execution of an order much cheaper for the client. Artificial intelligence, such as ChatGPT or Copilot, is a useful tool in creating truly unique masterpieces from mosaics. Images generated by artificial intelligence are never repeated. This makes it possible to create a unique design, giving individuality and character to any space. If artificial intelligence is used, mosaic in the interior becomes a type of digital art.