Трансформація банківських послуг у цифрову епоху: тренди та виклики

Biopharma Is Going Digital … Bit by Bit

A inovação tecnológica se mantém como relevante fator de crescimento econômico no contexto capitalista dinâmico, marcado pelo avanço das Tecnologias de Informação e Comunicação (TIC), no qual as organizações articulam capacidades dinâmicas na busca de vantagem competitiva sustentável, principalmente através de competências individuais. Nesse cenário, destacam-se a Indústria 4.0 (I4.0) e a Transformação Digital (TD), que inclui Big Data, Internet of Things (IoT) e Artificial Intelligence (AI), com ferramentas baseadas em algoritmos complexos, machine learning e deep learning. Podem servir à mineração de dados, análises preditivas e interação com pessoas através de Agentes Virtuais Inteligentes (AVI) que utilizam linguagem natural e aprendizado contínuo, formas de emular habilidades humanas complexas. Deste modo, os elementos da TD se apresentam como novas tecnologias que impactam a Organização do Trabalho (OT) e intensificam os desafios relacionados à integração da função estratégica da Gestão de Recursos Humanos (GRH), suas práticas e instrumentos, com exemplos de produtos (como HR analytics, people analytics e Artificial Intelligence, oferecidos por startups/spin-offs de HR Tech. Sob essas perspectivas, é proposto discutir o uso de IA (Computação Cognitiva) a partir do caso de um banco brasileiro, inicialmente através da percepção de seus funcionáros, atendidos por um AVI que presta informações sobre os produtos e serviços desta empresa, atividade normalmente atribuída aos atendentes do departamento de contact center. Como complemento, buscou-se observar se o uso dessa nova tecnologia, voltada a processos bancários, também inclui processos de RH.

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

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.

The digital transformation of healthcare is revolutionizing the management of medical institutions, improving operational efficiency, patient outcomes, and data security. With the increasing complexity of healthcare systems, the integration of cutting-edge technologies such as Artificial Intelligence. The COVID-19 pandemic significantly accelerated digital transformation, compelling healthcare institutions to adopt telemedicine, AI-assisted diagnostics, and cloud-based medical records to meet growing patient demands and resource constraints. The rapid digital transformation of healthcare is driven by advancements in Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and Big Data. This review article aims to analyze the objectives and implications of digital transformation in medical institutions, focusing on the integration of AI, blockchain, and IoT in hospital management. The methodological approach for this review article focuses on synthesizing existing literature to examine the role of Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and Big Data in the digital transformation of healthcare management. The integration of Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), and Big Data has demonstrated significant improvements in healthcare management, enhancing efficiency, patient outcomes, and data security.

Today's manufacturing sector undergoes essential changes due to emerging technologies and sustainability-focused practices alongside innovative operational methods. Industries worldwide are undergoing a substantial revolution as they adopt Industry 4.0 principles which involve extensive digitization and the implementation of artificial intelligence (AI). Traditional production methods are experiencing substantial transformations as Industry 4.0 principles and advanced technologies become integrated to increase industrial efficiency. According to Nathany (2024), global manufacturing output could achieve up to 25% growth through the adoption of Industry 5.0 technologies which also promises major operational cost savings. This research investigates the central elements of Industry 4.0 which consist of additive manufacturing, big data analytics, digital twins, artificial intelligence and machine learning, the Internet of Things (IoT), cloud computing, augmented and virtual reality along with blockchain technology. This study analyzes the applications of these technologies as well as their benefits and how they affect manufacturing processes. The research focuses on the ways that these technologies boost efficiency while improving sustainability and competitiveness in manufacturing. Manufacturing has experienced a revolutionary transformation through the integration of artificial intelligence and IoT which enables advanced defect detection capabilities as well as predictive maintenance and real-time monitoring. The method boosts human-machine teamwork alongside operational effectiveness and quality assurance while supporting personalized manufacturing and enhanced environmental sustainability (Nathany, 2024). This paper examines how sustainability and decarbonization will become essential factors in manufacturing's future path. Reducing environmental impact alongside optimizing resource use represents critical elements of the manufacturing industry’s transition. Additive manufacturing stands at the forefront of manufacturing evolution due to its infinite design capabilities and its efficient use of materials. Additive manufacturing generates substantial economic and environmental benefits through waste reduction and production customization. The research investigates the application of big data analytics for predictive maintenance and real-time monitoring and explains how these technologies increase production efficiency while minimizing breakdowns and unscheduled downtime. Big data analytics applications result in a 25% decrease in breakdowns and unscheduled downtime within manufacturing sectors as shown by Wang & Wang (2017). The study investigates how digital twins function as virtual models of physical assets to reveal their transformative capabilities. Real-time data analytics enable digital twins to optimize performance while predicting issues and detecting problems which then lead to substantial cost savings and enhanced planning efficiency and productivity. The advancement of digital twin technology reflects ongoing enhancements in product design and industrial manufacturing operations. Research shows digital twins can achieve 30% savings in costs while also reducing planning time by 20% and achieving 8% productivity enhancements (Kim, 2017). The discussion covers how cloud computing delivers essential computational power to support AI applications. Cloud technologies enable seamless data sharing between systems while providing adaptable solutions and support for manufacturing services that operate on demand. This research looks at how augmented and virtual reality systems can help manufacturing become more efficient by improving operational procedures and training programs through simulation-based methods. The research considers how blockchain technology supports sustainable operations while ensuring secure and transparent manufacturing networks.The study concludes by proposing future research pathways in Industry 4.0 domains which cover organizational management comprehensively and incorporate the latest organizational models. Innovative technologies will continue to generate vast opportunities for manufacturing growth and transformation. When manufacturers adopt these technological advancements they will achieve growth and competitive advantage while fostering sustainability.

Digital transformation is reshaping the oil and gas industry, offering powerful tools to enhance sustainability while maintaining operational efficiency. This review examines how digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and data analytics contribute to reducing environmental impact, improving resource management, and driving innovation in oil and gas operations. The sector faces increasing pressure to transition towards more sustainable practices due to regulatory demands, stakeholder expectations, and the global shift towards a low-carbon economy. In response, companies are adopting digital solutions to optimize energy use, minimize emissions, and enhance safety and transparency. Key technologies like AI and machine learning are instrumental in predictive maintenance, helping companies to foresee equipment failures and reduce operational downtime. IoT devices enable real-time monitoring of energy consumption and emissions, allowing companies to adjust operations to meet environmental standards more efficiently. Data analytics further enhances decision-making by providing insights into energy use, waste reduction, and resource allocation. These digital tools support a shift toward more circular economy models, where waste is minimized, and energy efficiency is maximized. Moreover, blockchain technology is being employed to ensure transparency and traceability in supply chains, enabling more sustainable procurement and resource management. Digital twins—virtual replicas of physical assets—are used to simulate processes, reduce risks, and improve performance while reducing environmental impacts. This integration of digital technologies not only helps companies meet regulatory compliance but also opens new avenues for innovation and competitiveness in a sustainable market. As oil and gas companies navigate the complexities of digital transformation, it is evident that these technologies are key to achieving long-term sustainability goals. This study highlights how digital transformation is crucial for creating a more sustainable future in the oil and gas industry by enhancing operational efficiency, reducing emissions, and fostering innovation.

The rise of digital transformation has posed challenges on organizations like never before due to the exponential growth, which has created the need to have a competitive edge that is obtained through using data in decision-making. The conventional business intelligence solutions have problems in managing the enormous amount, speed, and diversity of data produced by business organizations today leading to the provision of delayed insights and loss of opportunities. This literature review provides an analysis of the intersection of Artificial Intelligence, Big Data Analytics, Machine Learning, Internet of Things, and Blockchain technologies in transforming the business intelligence systems. The presented research applies a systematic review based on the PRISMA methodology in order to explore the opportunity of these new technologies to work together in order to enhance predictive analytics, automate business processes, guarantee information integrity, and generate business value never experienced before. Exploring a wide range of implementation applications in optimization of supply chains, customer experience improvement, fraud detection, and strategic planning, the review evaluates the implementation structures, technical architecture models and algorithm types. Among the core conclusions, it is possible to note that integrated AI-ML-Big Data environments will provide better predictive quality, IoT allows acquiring real-time operational intelligence, and Blockchain guarantees data provenance and trust. Nevertheless, there remain serious challenges such as quality of data, complexities of integrating the data, skills gap, ethical considerations and regulation compliance.

Intelligent organizational systems or intelligent organizations are being created through processes of digital transformation (DT) that typically involves intelligent technologies such as robotic process automation (RPA), the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), blockchain, drones, virtual reality (VR), 3D printing, big data and big data analytics (BDA). However, there is the significant problem of high DT failure rates with severe financial losses. Thus, understanding the key constructs for DT success in terms of organizational performance is an essential knowledge requirement and was the research problem and knowledge gap addressed by the study. The study followed an integrative literature review method since the purpose was analysis and synthesis for research model development. The study exposed the key constructs for successful DT in terms of organizational performance, namely organizational performance, sustainability, information technology (IT)/digital capability, change management, dynamic capabilities and risk management. This knowledge enables DT management to effectively manage DT toward success. For DT and related scientists, the study establishes the foundations for measuring DT and the associated organizational performance and facilitates DT theory progression and new knowledge generation.

This study delves into the crucial role of digital business transformation in enhancing organizational agility. As digital technologies rapidly advance, organizations face increasing pressure to adapt and innovate, making agility a key determinant of success in the modern business environment. The study's primary aim is to explore how digital tools, platforms, and strategies contribute to organizational flexibility, responsiveness, and competitiveness. Employing a comprehensive review of existing literature, the research examines the integration of digital technologies within organizational frameworks, highlighting the importance of leadership, culture, and continuous learning in facilitating agility. Key findings underscore the transformative impact of technologies such as artificial intelligence (AI), machine learning, the Internet of Things (IoT), and blockchain in driving real-time decision-making and fostering innovation. The study concludes that while digital transformation offers vast potential for enhancing agility, challenges such as legacy systems, resistance to change, and skill gaps must be strategically managed. The implications for practice suggest that organizations must adopt a holistic approach to digital transformation, aligning technological advancements with human capital development and cultural shifts to foster a resilient and adaptable business model. Recommendations for future research include exploring the long-term impacts of digital transformation on organizational structures and developing new models for assessing digital maturity and agility across various industries. This study provides valuable insights for practitioners and researchers, emphasizing the need for a strategic, integrated approach to digital transformation that aligns with organizational goals and fosters a culture of continuous innovation.

The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model

The Industrial Revolution 4.0, marked by the accelerated adoption of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, and other technologies, has significantly impacted various business sectors. One sector that has been fundamentally affected is accounting, which has had to adapt to these changes to remain relevant and efficient in an increasingly complex and dynamic business environment. This study aims to understand and analyze how digital transformation in the Industrial Revolution 4.0 era has affected the field of accounting. The research method used is descriptive-qualitative research. This research reveals that digital transformation in accounting, supported by technologies such as IoT, artificial intelligence, big data, and others, brings operational efficiencies through automation, increases the accuracy of financial data, and drives deep data analysis. Collaboration and communication are also enhanced through a cloud-based platform. However, data protection and privacy are important issues in the digital era. In the Industrial Revolution, 4.0, technologies such as big data analytics, AI, IoT, cloud computing, and blockchain are bringing fundamental changes, enabling accounting to be more proactive, using data analysis for better predictions and customer service, and more intelligent and responsive decision making. Digital transformation continues to change the landscape of the accounting and financial industry, making careful use of the potential of the digital age.

At the forefront of technological development, cloud computing, the Internet of Things (IoT), big data, artificial intelligence (AI), and blockchain (CTDAB) are not mutually exclusive, but complementary and integrated. The connotative characteristics and correlation of cloud computing, IoT, big data, AI, and blockchain are mainly introduced in this study, and their application in maritime field are summarized. Cloud computing, big data, and AI are the core technologies for realizing IoT technology. Blockchain technology supplements the functions of IoT. The cross-support and comprehensive application of these emerging technologies has started in the process of people’s exploration of the maritime field, which has played a key role in promoting the intelligent development of the maritime field.

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

As the number of patients treated in-home are increasing exponentially mainly in countries such as Japan, US A and Europe etc., these people often enter into a critical situation that may require help immediately (e.g. when facing an accident, or becoming depressed). Researches and Advances in computing areas and the Internet of Things (IOT) have provided efficient and cheap equipments including wireless communication and cameras, such as smart phones or embedded devices that enable the deployment of Health S mart Homes (HS H) that can provide medical treatment of patients in their homes. The images captured from these cameras can help nurses or caretakers of patients to provide timely help. The use of such patient images and emotional detection to assist patients and elderly people within home is provided in this article. Internet of Things and Cloud computing can work together, which can address to the Big Data problems. Big data is actually a term used for a huge data set that performs operations like storage of data, analysis, sharing, transfer, predictive analysis; updating etc. data set can grow rapidly. Data sets have different analytics on data that involve process of inspecting, transformation and modeling of data so as to discover new information, and to reach on some particular decision. There are some particular data analysis techniques from which data mining is a popular one which focuses on modeling and discovery of new facts. The discovered knowledge helps in predictive analysis purposes as well as text analysis. Further on data mining is performed on data set which involves discovery of patterns by applying different operations at databases, and using artificial intelligence, machine learning, statics etc. Keywords: Cloud computing, IOT, AI, face recognition, big data, data mining.