Understanding the potential of emerging digital technologies for improving road safety

This research paper examines the application of artificial intelligence (AI) in road network inventory and network-wide road safety assessment. An overview of current research related to this field is presented and various aspects of the use of AI in road infrastructure and safety assessment are analysed. In today’s world, where the road network continues to develop and grow, the use of AI can offer significant advantages in the inventory and assessment of road infrastructure and safety. AI can be used to process large volumes of data collected from various sources such as geographic data, photos, documentation and more. One of the main aspects that the article looks at is the use of AI to automatically process data and create a digital inventory of the road network. AI systems can analyse geographic data and photos to identify and classify roads, road signs, markings, and other elements of road infrastructure. This can facilitate engineers and competent authorities in the process of road inventory and maintenance. Another important aspect discussed in the paper is the use of AI for the general assessment of road safety. AI models can analyse road infrastructure inventory data, as well as traffic data, crashes, and other factors, to provide a comprehensive assessment of road network safety. This allows the competent authorities to identify vulnerable places, carry out preventive measures and plan appropriate interventions aimed at improving road safety. In conclusion, the paper highlights the possibilities and advantages of using artificial intelligence in road network inventory and network-wide road safety assessment. Such an approach can contribute to more efficient management of road infrastructure and improvement of road safety.

Digitalization penetrates all areas of economic activity, including the banking industry. Therefore, the purpose of the article is to analyze the transformation of banking services in the digital era based on an integrated approach. In the course of conducting scientific research, the authors used a set of scientific methods of cognition: the method of system analysis; the bibliographical method; the method of extrapolation and forecasting, which made it possible to identify patterns and trends in the development of digital transformation in banking. The digital transformation of banking leads to the introduction of innovative technologies to protect personal data, transaction data, the array of collected data, convenience and ease of use of mobile banking applications, speed of payments, etc. Such innovative technologies in the banking and financial sector are: mobile banking, AI, Blockchain, Machine Learning, Internet of Things, Big Data and open banking, etc. Each of the above areas of technological change contains the following characteristics: mobile banking – for digital interaction of banks with their clients; financial consultants based on artificial intelligence – to help in using the main functions of banking services, making informed management decisions; Blockchain – to reduce banking costs, the number of employees and increase the security of transactions; Machine Learning – to obtain a positive customer experience; Internet of Things – to integrate disparate types of equipment, devices for data exchange; Big Data – for collecting, processing, analyzing large data sets and making forecasts based on them and Open Banking – for the free exchange of financial data between different financial providers. The digital technology market is dominated by companies from the USA (Oracle, Microsoft, IBM, Amazon, etc.), as well as the UK and the EU. Forecasts of well-known consulting companies indicate the growth of digital technologies and the gradual transition to a virtual environment, where there is no need for the physical presence of the client in a bank. Keywords: digital transformation, banking structures, banking services, digital technologies, artificial intelligence, mobile banking, Blockchain technologies, Internet of Things, machine learning, big data, open banking.

OMICS: A Journal of Integrative BiologyVol. 24, No. 5 EditorialsCOVID-19 (Coronavirus Disease 2019): Opportunities and Challenges for Digital Health and the Internet of Medical Things in ChinaBiaoyang Lin and ShengJun WuBiaoyang LinAddress correspondence to: Biaoyang Lin, PhD, Department of Urology, University of Washington School of Medicine, Seattle, WA 98195, USA E-mail Address: biaoylin@gmail.comZhejiang-California International Nanosystems Institute (ZCNI) Proprium Research Center, Zhejiang University, Hangzhou, China.Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.Department of Urology, University of Washington School of Medicine, Seattle, Washington, USA.Search for more papers by this author and ShengJun WuDepartment of Clinical Laboratories, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.Search for more papers by this authorPublished Online:7 May 2020https://doi.org/10.1089/omi.2020.0047AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"COVID-19 (Coronavirus Disease 2019): Opportunities and Challenges for Digital Health and the Internet of Medical Things in China." OMICS: A Journal of Integrative Biology, 24(5), pp. 231–232FiguresReferencesRelatedDetailsCited byEmerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectivesBiomedical Signal Processing and Control, Vol. 83Integration of cloud computing with the Internet of things for the treatment and management of the COVID-19 pandemic15 November 2022 | Information Systems and e-Business Management, Vol. 26A novel e-healthcare diagnosing system for COVID-19 via whale optimization algorithm24 September 2022 | Journal of Experimental & Theoretical Artificial Intelligence, Vol. 6Call for Papers: COVID-19 Systems Biology, Multi-Omics Integration, and Digital Technologies in Ecology, Health, and Society Vural Özdemir7 September 2022 | OMICS: A Journal of Integrative Biology, Vol. 26, No. 9From Personalized Medicine to Population Health: A Survey of mHealth Sensing TechniquesIEEE Internet of Things Journal, Vol. 9, No. 17The role of contemporary digital tools and technologies in COVID‐19 crisis: An exploratory analysis6 October 2021 | Expert Systems, Vol. 39, No. 6Coronavirus Disease (COVID-19): Challenges and Opportunities10 September 2020 | Disaster Medicine and Public Health Preparedness, Vol. 16, No. 2Anthropomorphic Robotic Eyes: Structural Design and Non-Verbal Communication Effectiveness15 April 2022 | Sensors, Vol. 22, No. 8Digital Transformation in Personalized Medicine with Artificial Intelligence and the Internet of Medical Things Biaoyang Lin and Shengjun Wu7 February 2022 | OMICS: A Journal of Integrative Biology, Vol. 26, No. 2A Review About the Main Technologies to Fight COVID-1915 April 2022Identifying and addressing digital health risks associated with emergency pandemic response: Problem identification, scoping review, and directions toward evidence-based evaluationInternational Journal of Medical Informatics, Vol. 157Unlocking the Public Perception of COVID-19 Vaccination Process on Social MediaToward Combatting COVID-19: A Risk Assessment SystemIEEE Internet of Things Journal, Vol. 8, No. 21An online COVID-19 self-assessment framework supported by IoMT technology6 August 2021 | Journal of Intelligent Systems, Vol. 30, No. 1A Security Management Framework for Big Data in Smart HealthcareBig Data Research, Vol. 25Connected Medical Kiosks to Counter COVID-19: Needs, Architecture & Design GuidelinesThe perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and managementMaterials Today Chemistry, Vol. 20Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic15 April 2021 | Frontiers in Communications and Networks, Vol. 2A Comprehensive Review of Applications of Internet of Things for Covid-19 PandemicIOP Conference Series: Materials Science and Engineering, Vol. 1055, No. 1Robotics and Drone-Based Solution for the Impact of COVID-19 Worldwide Using AI and IoT16 February 2021Digital Transformation and Emerging Technologies for COVID-19 Pandemic: Social, Global, and Industry Perspectives20 February 2021Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic29 September 2021 | Primary Health Care Research & Development, Vol. 22Uma análise sobre o desenvolvimento de tecnologias digitais em saúde para o enfrentamento da COVID-19 no Brasil e no mundo1 January 2021 | Cadernos de Saúde Pública, Vol. 37, No. 3Enabled IoT Applications for Covid-191 May 2021Swarm Intelligence based Hierarchical Routing Protocols Study in WSNsCOVID-19 Digital Health Innovation Policy: A Portal to Alternative Futures in the Making Mustafa Bayram, Simon Springer, Colin K. Garvey, and Vural Özdemir3 August 2020 | OMICS: A Journal of Integrative Biology, Vol. 24, No. 8Digital Technologies-Enabled Smart Manufacturing and Industry 4.0 in the Post-COVID-19 Era: Lessons Learnt from a Pandemic3 July 2020 | International Journal of Environmental Research and Public Health, Vol. 17, No. 13Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature6 November 2020 | Journal of Medical Internet Research, Vol. 22, No. 11 Volume 24Issue 5May 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Biaoyang Lin and ShengJun Wu.COVID-19 (Coronavirus Disease 2019): Opportunities and Challenges for Digital Health and the Internet of Medical Things in China.OMICS: A Journal of Integrative Biology.May 2020.231-232.http://doi.org/10.1089/omi.2020.0047Published in Volume: 24 Issue 5: May 7, 2020Online Ahead of Print:April 20, 2020PDF download

Biopharma Is Going Digital … Bit by Bit

This book chapter delves into the dynamic landscape of Information and Communication Technology (ICT) in agriculture, offering a comprehensive overview of its applications and potential. It examines a wide range of ICT tools, including social media, print and electronic media, web portals, and digital agriculture platforms, highlighting their pivotal role in agriculture. Furthermore, the chapter explores the transformative technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Geographic Information Systems (GIS), Global Positioning System (GPS), blockchain technology, and big data analytics in the context of agriculture. By providing a primer on these technologies, it equips readers with a foundational understanding of their functionalities. The chapter emphasizes the importance of leveraging ICT and emerging technologies to optimize agricultural processes, improve resource efficiency, and enhance decision-making capabilities with the summary of successful integration and implementation of ICT in agriculture, promoting sustainable agricultural practices and food security in a rapidly evolving digital era

The Fourth Industrial Revolution (FIR) is radically changing the way people live, work and interact through the introduction of digital technologies that integrate into all spheres of society. ChPR is based on a complex of innovations, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, robotics, virtual and augmented reality, cloud computing, big data and 5G. These technologies make it possible to achieve new levels of automation, individualization and efficiency of production and business processes, as well as significantly increase the speed of information exchange and processing. Artificial intelligence opens up possibilities for the creation of intelligent systems capable of making decisions based on the analysis of large amounts of data, which are used in the financial sector, medicine, logistics, agriculture and the entertainment industry. The Internet of Things (IoT) creates "smart" ecosystems where everyday objects interact with each other over the network, providing users with new conveniences, from automated "smart" homes to "smart" cities. Blockchain, in turn, ensures the reliability and transparency of transactions and finds its application not only in the financial sphere, but also in logistics, legal transactions and even in voting systems. In addition, digital technologies contribute to the growth of manufacturing flexibility, which allows companies to quickly adapt to changes in demand, and robotics and automation create new production models where human labor is replaced by high-precision machines. With cloud computing, businesses have the ability to access powerful computing resources without significant capital investment, making innovation more accessible to small and medium-sized businesses. However, with the development of CSPR, new challenges arise, in particular, the threat of cyber security, the need to adapt personnel to new technologies, and the need for legislative regulation that protects users' personal data. The labor market is also changing: routine work is being replaced by automated systems, which requires improving the skills of employees and promotes the development of new professions. Digital CNC technologies change all aspects of our lives, transforming not only the economic model, but also the social structure, relations between countries, as well as developing a new level of technological interaction that can affect all spheres of society.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

This paper provides a comprehensive review of the future digitalization of microgrids to meet the increasing energy demand. It begins with an overview of the background of microgrids, including their components and configurations, control and management strategies, and optimization techniques. It then discusses the key digital technologies that can be used to improve the performance of microgrids, including distributed energy resources management systems, the Internet of Things, big data analytics, blockchain technology, artificial intelligence, digital twin technology, cloud computing, and augmented reality. The paper also highlights the importance of cybersecurity in microgrids, identifying the potential security vulnerabilities and threats to microgrid cybersecurity, as well as strategies for addressing these challenges. Finally, the paper discusses the barriers and challenges regarding the digitalization of microgrids, including technical complexity, high implementation costs, regulatory barriers, data privacy and security concerns, lack of standardization, interoperability issues, limited technical expertise, and integration with the main grid. Overall, this paper demonstrates the significant potential for digital technologies to transform the future of microgrids. By leveraging advanced technologies and implementing effective cybersecurity measures, microgrids can become more efficient, reliable, and resilient, enabling them to meet the growing demand for energy and contribute to a sustainable energy future.

Over the recent years, firms have showed an increased demand for leveraging the performance potential of Artificial Intelligence (AI) technologies. However, the general applicability of AI to many settings raises important managerial questions about whether aspects such as potential for in-house versus external AI development, the presence of complementary technological resources (e.g. access to cloud computing), or even the specific use of AI (e.g. product/service development, operational management) would guide firm-level decisions underlying AI adoption. While recent research has provided some insight on worker-level decisions about using AI, knowledge about firm-level AI adoption, as well as heterogeneities in adoption, remains scant. To address this empirical gap, this preliminary paper provides one of the first comprehensive assessments of firm-level determinants of AI adoption while accounting for the heterogeneous sources, applications, and complementary technological resources of AI in organizations. Specifically, this paper reports the first results of a novel firm-level AI adoption survey deployed to a representative sample of South Korean firms from 2017 and 2018. Econometric assessments identified that firm size and use of existing intangible assets are important for AI adoption. However, while these characteristics are significant for AI adoption regardless of whether the technology was produced in-house or obtained from a vendor, analyses suggest that there may be heterogeneities on the determinants of AI adoption depending on the operational application of AI. Furthermore, AI adoption also ensues in tandem with the adoption of other digital technologies (e.g. big data, cloud computing, and the Internet of Things) and with contemporaneous firm reorganization.

Today’s cities are facing the challenges of increasing traffic congestion, emissions, and the need to improve road safety. The solution to these problems is the use of artificial intelligence (AI) and the Internet of Things (IoT) in intelligent traffic management. The purpose of the article is to analyze and evaluate AI- and IoT-based solutions implemented in Polish cities and to identify innovative proposals that can improve traffic management. The study uses a mixed-method approach, including the analysis of crowdsourced mobility data (from GPS, smartphones, and municipal reports), GIS tools for mapping congestion, big data analytics, and machine learning algorithms, to evaluate trends and predict traffic scenarios. The evaluation focused on seven major Polish cities—Warsaw, Krakow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz—where intelligent transportation systems such as dynamic traffic lights, intelligent pedestrian crossings, accident prediction systems, and parking space management have been implemented. The effectiveness of these solutions was assessed using the following six key indicators: waiting time at intersections, travel time, congestion level, CO2 emissions, energy consumption, and number of traffic incidents. The article provides a comprehensive analysis of these solutions’ impacts on traffic flow, emissions, energy efficiency, and road safety. A key contribution of the paper is the presentation of new proposals for improvements, such as the inclusion of behavioral data in traffic modeling, integration with GPS navigation, and dynamic emergency and public transport priority management. The article also discusses further digitization and interoperability needs. The findings show that the implementation of intelligent transportation systems not only improves urban mobility and safety but also enhances environmental sustainability and residents’ quality of life.

The usage of digital technology has been aided by the growing number of Muslim tourists around the globe each year. The birth of the Fourth Industrial Revolution has shifted consumer behaviour and attitudes concerning the use of digital technology in their daily lives. The Halal tourism business is facing various hurdles as a result of the global economy's transition owing to technological disruption. Thus, the purpose of this chapter is to explore the various digital technology utilised in Halal tourism as well as the issues that the business faces. Through the exhaustive study of existing research and contextual synthesis, cyber-physical systems, the internet of things, cloud computing, big data, artificial intelligence, and advanced robotics are among the digital technology utilised to boost Halal tourism. Meanwhile, Muslim tourists are at risk of safety and security, digital technology providers are facing a hard time keeping facilities up to date, Halal terms required standardisation and regional marketing programmes are among the issues Halal tourism stakeholders face in adopting digital technology. Since the research on Halal tourism is currently mounting, this study could contribute to that body of knowledge and provide useful information to both practitioners and scholars.KeywordsHalal tourismTechnology adoptionTourism 4.0Sustainable tourismTourism resilience

The research investigated the role of digital technology in enhancing sustainable business models, collective intelligence, and scaling to improve business process performance. Conducted as qualitative research, it employs a Systematic Literature Review (SLR) method, analyzing 20 Scopus-indexed journals ranked in Quartile 1 (Q1) to Quartile 2 (Q2) from 2019 to 2024. The data were processed using content analysis, focusing on digital technologies such as big data, the Internet of Things, cloud computing, cyber-physical systems, machine learning, artificial intelligence, and digital platforms. The findings demonstrate that digital technology supports businesses in understanding evolving customer needs, facilitating efficient and accurate decision-making, and enhancing customer engagement, which is crucial for business growth and scalability. Key benefits include improving customer experience, segmentation, retention, and engagement, reducing operational costs, increasing team collaboration, and evaluating environmental risks. These advantages give businesses a competitive edge by fostering sustainable practices and effective customer engagement. The advantages of implementing digital technologies can be felt from various economic, social, and environmental aspects. However, the research acknowledges limitations, including restricted data collection due to limited relevant research and variations in technological literacy across regions. Despite these challenges, the research underscores the transformative role of digital technology in advancing sustainable and competitive business processes.

PurposeThe aim is to analyse a regional cultural tourism object in Latvia, focusing on the elements of smart tourism as an indication of opportunities provided by digital technologies and its practical application.Design/methodology/approachThe research employed data from the Central Statistical Office of the Republic of Latvia (hereinafter CSB), publicly available reports by ministries of the Republic of Latvia (hereinafter RoL), the Organization for Economic Cooperation and Development (hereinafter OECD) etc., as well as summarized scientific findings on the research problem. Data on the use of smart technology elements at the Rundale Palace Museum were obtained in two ways: from public reports on the museum in 2016–2021 and additionally an expert interview was conducted with the deputy director of the museum. The expert interview questions were semi-structured.FindingsThe characteristics of cultural tourism indicate the use of ICT in destination management, marketing, planning, process organization and changes in organizational culture. The recognizable elements of smart cultural tourism are the smart travel destination, smart experiences and smart business. 4.0 G technologies are required to ensure the functioning of the elements: artificial intelligence, big data analytics, the Internet of things, blockchain, cloud computing, virtual and augmented reality. Smart cultural tourism is a unified system involving several levels of national and municipal institutions, organizations, entrepreneurs and NGOs. The implementation of smart cultural tourism requires centralized funding to implement and manage digital connectivity between the stakeholders. In the Latvian and Baltic context, the Rundale Palace Museum has been established as a cultural tourism object – a Renaissance palace museum, which is the second most visited museum in Latvia. The Rundale Palace Museum digitizes the services it provides within its financial possibilities, e.g. cloud data for remote purchase of visitor tickets, self-registration of visitors in the single visitor system, augmented reality and mobile applications. The Internet of things is not used, while artificial intelligence is partially used by the museum. At the Rundale Palace Museum as a smart tourist destination, the research did not identify the following features: a technological platform, a smart destination strategy and use of big data. Further, public-private consumer cooperation is not in place. The Rundale Palace Museum was not identified as an element of smart business, as a single smart business ecosystem for cultural tourism has not been created in the entirety of Latvia as yet nor in the Zemgale region in particular. Communication between cooperation partners occurred via telephone.Originality/valueFew research studies on digital solutions for cultural tourism in Latvia and in the region of Zemgale, where the largest Renaissance pearl in Latvia and the Baltic States – the Rundale Palace Museum – is located, have been conducted; hence, this study contributes to addressing the research gap.

Digital transformation has become a buzzword across many industries, and the dental field is no exception. Based on electronic health data, digital transformation is acknowledged as one of the biggest game-changers of the twenty-first century, tackling both current and future challenges in dental and oral healthcare. A novel method for addressing today’s major healthcare issues, such as an ageing population with a higher incidence of chronic diseases and higher lifetime treatment costs, is offered by the utilization of digital tools and apps. Health care providers may improve patient satisfaction, develop loyalty and trust, and streamline operations with the aid of digitalization. The trend of digitalization has also been influenced and nurtured by the social and cultural habits of civilised society in industrialised nations. These behaviours include urbanism, centralization, mobility, and constant accessibility through the use of smartphones, tablets, and the internet of things (IoT). In order to ensure transparency for all parties involved—including patients, healthcare providers, universities and research institutions, the medtech industry, insurance, the public media, and state policy— digital dentistry necessitates managing expectations in a more pragmatic and realistic manner. It is not to be construed that digital smart data technology will eventually take the place of people who can provide dental competence and patient empathy. The dental team in charge of digital applications is still crucial to patient care and will always be so. Many difficulties arise in the process of gathering, storing, and analysing digital biological patient data. Safeguarding patient data for optimal safety requires not just technical considerations for managing massive volumes of data, but also adherence to globally established norms and ethical guidelines.1 There are four main categories that best describe the advantages of digital dental technologies in Prosthodontics. The first and foremost is improved communication. Dental laboratory staff, patients, dentists, and other stakeholders may all communicate clearly with the help of computerized patient records. Furthermore, digital radiographs and pictures depicting intraoral conditions improve the information exchange between medical professionals and patients. One of the main benefits of integrated electronic patient records is error-free, real-time communication. Enhanced record keeping, data fidelity, workflow efficiency, and therapeutic outcomes are among the benefits of increased quality. Real-time clinical improvement is made possible by intraoral scanning of tooth preparations that are examined in highly contrasted, magnified fields on a computer screen while the patient is in direct view. Data archiving for specific patients is the third benefit. The following are some benefits of using 3-D archived diagnostic casts: a) long-lasting images without causing damage or loss to the original casts; b) allowing the images to be interfaced with other images for analysis using cutting-edge analytical and design tools; c) removing human error; and d) reduced costs for storage. The fourth and most significant advantage of digital technology in Prosthodontics is its favourable effect on patient satisfaction. More advanced treatment plans are driven by the enhanced diagnostic data. Several factors, such as time constraints, IT support costs, a lack of basic computer skills, disruptions to workflow, privacy and security concerns, interprofessional and intersystem connections, and technical and expert support, are impeding the adoption of digital technology in Prosthodontics.2,3 Several digital processes for production processing in Prosthodontics have already been incorporated into treatment procedures, particularly in the quickly expanding fields of rapid prototyping (RP) and computer-aided design/computer-aided manufacture (CAD/ CAM). Artificial intelligence (AI) and machine learning (ML) have created new opportunities for automated processing in radiological imaging. Furthermore, the technology underlying the superimposition of various imaging files to create virtual dentistry patients and non-invasive simulations comparing various outcomes before any clinical intervention is known as augmented and virtual reality, or AR/VR. These exciting new technologies—whose potential applications are still up in the air—have been made conceivable by increased IT capability.1,2,3 The process of rapidly and autonomously creating three-dimensional (3D) models of a finished product or a component of a whole using 3D printers is known as rapid prototyping. Complex 3D geometries can be produced at a reasonable cost with minimal material waste, thanks to the additive manufacturing technique. The workpiece is virtually sliced into multiple two dimensional layers. The tool-path is then generated by the AM machine in both the x and y dimensions. A three-dimensional component is formed by sequentially depositing each material layer on top of the other. The foundation of this novel approach is the slicing of a three-dimensional CAD model into numerous thin layers, which are then built one after the other by manufacturing machinery using the geometric data. Dental technology can benefit greatly from RP’s mass manufacture of dental models and its ability to fabricate implant surgical guides. Large-scale, simultaneous production in a repeatable, standardized manner is highly advantageous from an economic perspective.4 Augmented reality, or AR, is an interactive technology that uses computer-animated perceptual data to enhance a real-world experience. Stated differently, augmented reality is the addition of virtual content to the physical world. Usually, it involves superimposing extra digital data on real-time pictures or movies. In contrast, virtual reality relies solely on artificial, non-reality-connected computerized settings. Every imaginable form of sensation, primarily visual, aural, and haptic, can be employed alone or in any combination, depending on the technique. In addition to several fascinating advancements for patients and healthcare professionals, AR/VR technologies are currently finding a growing number of applications in the field of Prosthodontics as a whole.5,6 Artificial Intelligence has come a long way in the last ten years. The field of Prosthodontics is about to benefit from the most intriguing AI applications that are just around the corner. Though AI is developing quickly, it will never be able to fully replace human intelligence, skill, or capacity to make decisions. Artificial Intelligence (AI) in prosthodontics is growing exponentially. The implementation’s results are on par with, and sometimes even better than, those of humans. AI can be seen as a potential tool in every area, including the identification of marginal lines, the classification of denture fixtures and maxillofacial prosthesis, and the reduction of human error in implant cementation. Furthermore, AI cannot take the role of human knowledge, skill, or treatment planning; it can only support clinicians in carrying out their responsibilities in a professional manner. AI is generally recognized as a great tool for Prosthodontists, despite the fact that there are still obstacles to be addressed, including data collection, interpretation, computing power, and ethical issues. With careful design and long-term clinical validation, AI can be transparent, unbiased, repeatable, and user-friendly.7,8 Future research should emphasize the connection between oral and general health in order to concentrate on patient-centered outcomes and personalized therapy. Research in Prosthodontics ought to be useful to society in this context. It shouldn’t only produce papers for scientific journals; instead, it should aim to improve clinical protocols. Research and development in material science and related technical applications aim to preserve tooth structures with early diagnosis, repair of dental conditions to attain aesthetics, function with high degree of predictability, along with fewer appointments. Digital technology has a significant impact on patient motivation, clinical aspects, laboratory procedures, student training, practice management, and research.

Impact of emerging technologies on digital manufacturing: Insights from literature review