Digitale Innovation in der Medizin – die COVID-19-Pandemie als Akzelerator von „digital health“

In the era of continuous development of computer technology, the application of artificial intelligence (AI) and big data is becoming more and more extensive. With the help of powerful computer and network technology, the art of visual communication (VISCOM) has ushered in a new chapter of digitalization and intelligence. How vision can better perform interdisciplinary and interdisciplinary artistic expression between art and technology and how to use more novel technology, richer forms, and more appropriate ways to express art has become a new problem in visual art creation. This essay aims to investigate and apply VISCOM art through big data and AI methods. This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. STING is a multi-resolution clustering technique for big data, with the advantage of efficient data processing. In the experimental part, this essay selected a variety of design contents in VISCOM art, including logo design, text design, scene design, packaging design and poster design. STING and CNN algorithms were used to cluster and AI-identify the design elements 16 of the design projects might contain. The results showed that the overall average clustering accuracy was above 82%, the accuracy of scene element recognition mainly was above 80%, and the accuracy of facial recognition was above 80%; this showed that this essay applied AI and big data to the design of VISCOM, and had a good effect on the clustering and identification of design elements. According to expert scores, these applications' reliability and practicality scores were above 70 points, with an average of about 80 points. Therefore, applying big data and AI to VISCOM in this essay is reliable and feasible.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

PurposeThe purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

The integration of artificial intelligence (AI) and Big Data is emerging as a transformative force in the global food industry, driving sustainable growth through enhanced efficiency, productivity, reducing waste, improving resource management and decision-making capabilities. Recent advancements in AI and Big Data technologies, such as predictive analytics and machine learning, are revolutionizing agricultural practices by enabling precision farming, optimizing resource use and improving crop management systems. These technologies facilitate real-time monitoring of crop health, yield predictions and disease detection, thereby addressing critical challenges such as food insecurity and waste reduction. The application of AI and Big Data in the food supply chain enhances traceability and transparency, which are essential for ensuring food safety and quality. These technologies have further accelerated the adoption of digital solutions in the food sector, highlighting the need for resilient supply chains capable of adapting to disruptions. As the food industry grapples with the dual pressures of climate change and a growing global population, the role of AI and Big Data in promoting sustainable practices becomes increasingly vital. In addition to agricultural applications, AI and Big Data are reshaping business models within the food industry by fostering innovative marketing strategies and personalized nutrition solutions. The convergence of these technologies not only supports environmental sustainability but also enhances economic viability, paving the way for a more sustainable food ecosystem. Incorporating AI and Big Data into the global food industry fosters resilience against challenges such as climate change, resource scarcity and population growth. Therefore, by facilitating more sustainable and efficient operations, these technologies are revolutionizing food production, processing, distribution and consumption, thereby aligning the industry with the principles of environmental stewardship and global food security, in accordance with global sustainability objectives.

The purpose of this study is to explore how artificial intelligence (AI) and big data can be used to solve the twin issues of athlete safety and sports event quality in a college sports environment. Furthermore, this study attempts to fill the literature vacuum regarding the application and effectiveness of artificial intelligence and big data in improving safety and quality in collegiate sports administration by investigating possible synergies between these elements and the implementation of developed technologies. This qualitative study used a sampling method to conduct in-depth interviews with 18 sports administrators and commentators. Using coding and classification methods, the data were evaluated thematically with a focus on artificial intelligence and big data applications. Research has found that artificial intelligence and big data play a key role in proactively reducing injuries, optimizing athlete performance and enabling data-driven decision-making. It also identifies barriers and opportunities for integrating these technologies, revealing their dynamic potential. This study provides new perspectives on the relationship between safety and quality and the application of artificial intelligence and big data in collegiate sports management. It also highlights the ways in which these technologies have transformative potential in sport. The findings have important implications for educational programs and policy development aimed at managing responsible technology integration and preparing future professionals in the field of sport management.

Given the impacts of information and communications technologies on the planet and people, researchers from all disciplines need to pay attention to the environmental footprints and sustainable opportunities associated with the Artificial Intelligence (AI) and Big Data applications. In order to provide a preliminary discussion on the eco-design of AI and Big Data applications, the paper aims to map out the sources and topics to explore (1) the intersections of eco-design with AI and Big Data, and (2) the sustainable and eco-friendly design. Based on a bibliometric analysis of 236 articles gathered from the Web of Science (WoS) database, the paper has identified main research topics, publication outlets, research fronts, and intellectual bases and explored their inter-relationship and grouping. The leading application disciplines include construction & building technology, automation & control system, computer & information science, and business & economics. Multi-disciplinary and inter-disciplinary journals such as Sustainability, Journal of Cleaner Production, and Sustainable Cities and Society have been proven to be more receptive to such eco-design research outputs. As the notion of eco-design of AI and Big Data applications is expected to be pertinent and relevant for designing greener strategies, products and services for green digital transformation, we argue that more concerted efforts must be made to advance both the theoretical and empirical research on the nascent topic among researchers, funding bodies, policy-makers and industry professionals.

The eruption of COVID-19 Corona Virus, namely SARS-CoV-2, has created a disastrous condition throughout the world. The cumulative incidence of COVID-19 is increasing rapidly day by day all over the world. Technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big Data and Deep Learning can support healthcare system to fight and look ahead against fast spreading of new disease COVID-19. These technologies can significantly improve treatment consistency and decision making by developing useful algorithms. These technologies can be deployed very effectively to track the disease, to predict growth of the epidemic, design strategies and policy to manage its spread and drug and vaccine development. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this study aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. This study first presents an overview of AI and big data along with their applications in fighting against COVID-19 and then an attempt is made to standardize ongoing AI and deep learning activities in this area. Finally, this study highlighted challenges and issues associated with State-of-the-Art solutions to effectively control the COVID-19 situation.

The COVID-19 pandemic has severe impacts on global health and social and economic safety. The present study discusses strategies for turning the COVID-19 crisis into opportunities to use artificial intelligence (AI) and big data in business operations. Based on the shared experience and theoretical ground, researchers identified five major business challenges during the COVID-19 pandemic: production and supply-chain disruption, appropriate business model selection, inventory management, budget planning, and workforce management. These five challenges were outlined with eight business cases as examples of companies that had already utilized AI and big data for their business operations during the COVID-19 pandemic. The outcomes of this study provide valuable insights into contemporary social science research and business management with AI and big data applications as a business response to any crisis in the future.

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

With the rapid development of artificial intelligence (AI) and big data technologies, Python has become a widely used programming language in related fields. However, the traditional "Python Programming" course has been unable to meet the demands of applications in AI and big data in terms of content and teaching models. Therefore, this paper presents a comprehensive reform of the "Python Programming" course, optimizing course content by incorporating modules on big data processing and artificial intelligence. Additionally, a project-driven teaching model and a diversified evaluation system have been implemented. By introducing innovative teaching tools and resources, students' practical abilities and innovative thinking have been enhanced. The practical outcomes of the course reform are significant, with students showing notable improvements in their programming skills in AI and big data. In the future, this study will continue to optimize course content to address the ever-changing technological demands.

As artificial intelligence and big data technologies are rapidly changing the face of financial management. This paper delves into the application of artificial intelligence (AI) and big data technologies in modern financial management, revealing how these two technologies can reshape the financial management process, improve management efficiency and decision-making quality, and create greater economic value for enterprises. The core of the study focuses on the specific applications of AI and big data in financial data processing, risk identification and assessment, predictive analytics, and intelligent decision support, as well as how these applications can contribute to the intelligence, precision, and efficiency of financial management.The significance of this study lies in the fact that it provides new ideas and methods for enterprise financial management practices and promotes the intelligent transformation of financial management. By deeply analysing the application cases and effects of AI and big data in financial management, this paper provides valuable references and lessons on how enterprises can effectively use these technologies to improve their financial management.

In today's competitive entrepreneurial landscape, effective marketing communication strategies play a pivotal role in success. Entrepreneurs are increasingly adopting cutting-edge technologies like artificial intelligence (AI) and big data analytics to optimize their marketing efforts. This research explores the synergistic impact of AI and big data analytics on marketing communication strategies within entrepreneurial ecosystems, presenting a data-driven approach. The study assesses the current marketing communication landscape in entrepreneurial ventures, identifying challenges faced by entrepreneurs in connecting with their audiences. By reviewing the latest trends in AI and big data applications, we investigate their integration into marketing communication strategies. Through case studies and empirical data analysis, the research uncovers the successful adoption of AI and big data analytics in entrepreneurial marketing communication. These technologies enable personalized and targeted campaigns by identifying customer preferences and behaviors. Big data analytics helps refine marketing strategies by extracting valuable insights from vast datasets. The research also addresses challenges and ethical considerations related to data privacy and bias. Additionally, it explores the necessary infrastructure and human capital for effective implementation. The findings highlight AI and big data's critical role in driving innovation and growth in marketing communication within entrepreneurial ecosystems. Adopting a data-driven approach empowers entrepreneurs to enhance marketing effectiveness and gain a competitive edge. In conclusion, this research showcases AI and big data analytics as transformative tools for shaping marketing communication in entrepreneurial ventures. Leveraging these technologies strategically can unlock novel opportunities and ensure long-term business success in the dynamic marketplace.

The application of artificial intelligence (AI) and big data in geohazard investigations has gained popularity due to the development of machine learning algorithms and data collection methods. Previous studies have compared and applied various machine learning‐based methods, such as conventional machine learning, deep learning, and transfer learning in different areas. This special issue provides state‐of‐the‐art information on the use of AI in geotechnical research, particularly in the Three Gorges Reservoir (TGR) area and adjoining regions. The aim of this volume is to serve as a reference for future researchers interested in exploring the potential of AI in geohazard investigations. It is hoped that this special issue will contribute to the development of guidelines for enhancing the application of AI and big data in geotechnical research, thereby improving our understanding of geological terrains and their associated hazards.

This paper focuses on the application and innovation of big data and artificial intelligence in the field of human resource management. With the continuous development of information technology, big data and artificial intelligence have become indispensable and important tools in enterprise management. In the field of human resource management, the application of big data and artificial intelligence provides enterprises with more accurate and efficient management methods and promotes the innovation and development of human resource management. This paper first introduces the basic concepts and characteristics of big data and artificial intelligence, and then analyzes their applications and innovations in recruitment, training, performance management, and employee benefits. Next, the paper discusses specific cases of big data and artificial intelligence in human resource management and evaluates their effects and impacts. Finally, the paper summarizes the roles and challenges of big data and artificial intelligence in human resource management and looks ahead to the future.