Digital transformation is widely understood as a process where digital technology, such as smart sensors, digital monitoring and surveillance tools, ecosystems, the cloud, the Internet of Things, simulation, artificial intelligence (AI), digitalization, and datafication are used by an organization to modify its processes, products, and services to create new ones [1]. According to researchers, digital transformation changes the ways people communicate, learn, and participate. It also raises business and administration efficiency and transforms the organization’s structure and customer relationships [2-5]. Furthermore, digital transformation benefits the whole society by providing better and accessible public services; therefore, it has the power to improve health care and personal health management [3,4,6].

Digital technology is undoubtedly an enabler for digital transformation, but results depend on how and by whom technology is used, as well as the ideas, beliefs, and goals behind decisions. It is often presented as an unavoidable and one-way result of the evolution of digital technology, but this is a misunderstanding. In the business world, digital transformation is increasingly realized using ideas of free market economy, digital information capitalism, and neoliberalism [7].

The use of information technology in health care has a long history. Presently, technologies such as medical imaging, smart medical devices, internet, digital platforms, AI, robotics, big data analytics, blockchain, and smart wearables are widely used [8,9]. Digital transformation in health care is not limited to the use of digital artifacts; it also includes the advancement from data focus to knowledge focus [10]. It is expected to change health care processes and structures; enable personalized services and innovative solutions, such as online remote monitoring, virtual care, digital physician, and health chat boxes; advance diagnosis; and improve planning and management [11]. It changes the patient-physician relationship and responsibilities, including where the health data are processed and by whom, medical education, and patient engagement [3]. It also enables companies to develop new medical devices and innovative digital services for health care as well as commercial devices and applications for personal health and wellness monitoring and management, such as smartwatches, heart rate monitors, blood pressure monitors, electrocardiogram monitors, sleep trackers, smart wearables, and medication reminders [12].

Digital health (DH) is the end result of ongoing digital transformation in health care and health [10]. According to the World Health Organization, it “can revolutionize how people worldwide achieve higher standards of health and access services to promote and protect their health and well-being and have proven potential to enhance health outcomes” [8]. It has the potential to improve patients’ overall health care experience and to offer personalized care everywhere [13].

The ongoing discussion of digital transformation in health care and health is associated with positive promises, such as higher efficiency, lower costs, easy availability and equal access to services, easy personal health management, new innovative services, and better data-driven decision-making [14,15]. These promises are merchandized to decision makers and society as an inevitable outcome of technological determinism aimed at digitalizing human life, body, and mind [16]. At the same time, discussion of possible negative consequences is largely neglected, and they are presented as things to be solved in the future. According to researchers, digital transformation, datafication, digital tools, and algorithms have already enabled almost unlimited data surveillance and tailored nudging of people. It has provided a huge amount of power to data collectors and algorithm owners, and its negative impacts on health care and health are immense. This has raised concerns about human rights, privacy, and autonomy [17]. For managing this problem, the IEEE P7008 Standard for Ethically Driven Nudging for Robotic, Intelligent, and Autonomous Systems should be deployed.

In this paper, we aimed to present detailed insights into the digital transformation of health care and health to DH and develop a new model called human-centric DH (HCDH) that provides most of the promised benefits of DH without the loss of human rights, privacy, dignity, and autonomy.

In this paper, DH was studied as a holistic system. Up to 560 research articles published in major journals covering a wide range of topics such as human rights, dignity, privacy and autonomy, neoliberalism, digital transformation, digital technology, DH care, DH, and digital twin were reviewed in detail. We analyzed ideas, beliefs, and goals guiding the transformation of health and health care to DH and their possible consequences on information privacy, human rights, dignity, and autonomy. On the basis of the analysis performed, a framework of 6 views that impact the nature and functions of DH was developed (Figure 1). Ideologies, that is, beliefs and opinions, form the base of transformation. The “data view” is derived from the fact that successful DH requires a great deal of health-related data to enable its services. Guiding rules, principles, and norms (eg, human rights, laws, and regulations) both direct and set restrictions concerning what data can be collected and used. The “architectural view” indicates that DH is an information system that requires a conceptual, organizational, and technical architecture. Finally, technological, architectural, and organizational innovations are enablers for the realization of DH as the system that fulfills its ideas and goals.

DH itself is a concept with many definitions. In a review by Fatehi et al [18], they found 95 different definitions of DH. A narrow health care–focused definition expresses that DH is “the cultural transformation of disruptive technologies that provide digital and objective data accessible to both caregivers and patients” [19]. According to Yeung et al [20], DH is a broad term covering the application of digital technologies in the context of health based on the definition for health (“a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity”) by the World Health Organization [21]. This definition expands the concept of DH to cover health determinants other than health care services (eg, environment, genetics, and social relations) [22]. It also encompasses uses of digital technologies, such as the internet, big data analytics, AI, and robotics [20]. Because access and use of health care services have a much lesser impact on human health than other health determinants, personal wellness lifestyle management, and digital technology that is used to collect and use personal health information should also be included in the concept of DH [20,23]. In this paper, the definition by Fatehi et al [18], “Digital Health is proper use of digital technology for improving the health and well-being of people at individual and population levels, as well as enhancing,” is used [18].

In spite of health care’s strong professional ideology, its service providers are increasingly looking not only for better service accessibility and higher quality but also higher efficiency, optimal allocation of resources, lower costs, and possibilities of digital technology. This indicates that economic ideologies are increasingly transforming health care.

Classical capitalism is an economic model based on private ownership of property and business. Its goal is the maximization of its own (monetary) utility without responsibility for possible negative consequences [24]. Free market and weak state control are its additional elements. According to Sadiku et al [25], digital capitalism (ie, platform capitalism, informational capitalism, and surveillance capitalism) is a natural extension of classical capitalism [15]. It uses digital technologies, such as the internet, AI, digital algorithms, blockchain, digital platforms, and the Internet of Things for data collection, processing, and sharing [26]. Therefore, data about people’s activities, behaviors, lifestyle habits, and emotions are both a meaningful commodity and product, and it is expected that in the long run, all aspects of human life can be quantified and transformed into information for private or public benefit [26,27].

According to McGregor [7], neoliberalism is the logical evolution of digital capitalism. Neoliberalists believe that social good will be maximized by maximizing “the reach and frequency of market transaction,” and a “good life” arises from “free” individuals under a free market system [28]. At the ideological level, neoliberalism’s aim is the commodification of goods, services, and knowledge, and reducing most aspects of human lives into “goods” (ie, datafication) [28]. Core ideas in neoliberalism are privatization of public services, deregulation, marketization, self-regulation, strong private property rights, free markets and trade, the removal of regulatory barriers to commerce, and maximizing efficiency. Furthermore, the government’s main role is to boost private-sector business and weaken regulations. Personalized data are collected to generate behavioral changes. One of neoliberal goals is to commodify traditional public services [29]. Neoliberalists replace the concepts of the public good and the community with individual responsibility based on self-interest and individualism [7]. It is also assumed that humans will always try to favor themselves, and that they do not feel the need to consider the consequences of their actions toward others [7].

Because a market economy and neoliberalism are prevailing and dominating ideologies in many sectors of society, a likely scenario is that DH will be built around beliefs and goals of a market economy and neoliberalism. In this paper, this scenario (“neoliberal DH”) is used as an example. In health care and health, this means that services are organized using the economic free market model and privatization, monetization of patient data, and datafication of all health data as the most effective solutions to organize and offer equal health care services and treatments for all [7]. Another idea is consumerization, where people are understood as rational health consumers having the ability and capacity to self-manage their health and sickness. Commodification is also necessary for better care and lower cost, especially the commodification of the content of regulated health care records, as well as other personal health-related data. The idea of health care as a public good is replaced by the idea of individual responsibility for health and health care as private goods. Datafication of all health data is necessary for disease prediction, personalized care, early detection of diseases, modification of people’s health behaviors, creating digitally engaged patients, and earning money [30].

According to Stanley et al [31], normative ethical theories and religious traditions offer general moral principles for people to follow. The United Nations’ human rights are universally accepted and used in many international and national laws as guiding principles and norms for human behavior. Human rights are basic rights and important for human flourishing, individual freedom, and social justice [32]. Human rights and dignity are 2 interlinked concepts. In the preamble of the United Nations Declaration of Human Rights (UNDHR), human dignity is the inherent dignity, that is, human rights are derived from human dignity, as human dignity is the foundational principle of human rights and the wellness of society. They are needed to prevent injustice, exploitation, discrimination, and inequality [33,34]. Examples of human rights are the right to life, equality before the law, liberty, personal security, the right to education, and the right to free movement. Among the 30 articles of the UNDHR, articles 12 and 18 are especially meaningful for DH [35]. According to article 12, “No one shall be subjected to arbitrary interference with his privacy, family, home, or correspondence, nor to attacks upon his honor and reputation. Everyone has the right to the protection of the law against such interference or attacks,” and according to article 18, “Everyone has the right to freedom of thought” [35,36]. Furthermore, governments have obligations to protect human rights everywhere, including in the digital context [2,35,37].

Dignity itself is a multidimensional and vague concept. At the general level, dignity concerns “moral status involving inherent unearthed form of worth that is independent of the interest of others and not based on one’s merits” [38]. The idea of the intrinsic human worth of honor or respect is global and exists in some way in all cultures [34]. Dignity is also linked to personal characteristics and collective behaviors in social relations. Human dignity gives moral weight to the idea that people cannot be owned as property by others [39]. Furthermore, the European Union Charter of Fundamental Rights declares that human dignity is inviolable and must be respected and protected [40].

Autonomy includes concepts such as self-rule, self-determination, individuality, and independence [41]. Furthermore, it addresses the right of people or a group to govern themselves or to organize their own activities [42]. According to the UNDHR, bodily autonomy is a fundamental right. Individual autonomy is widely understood as the capacity to be one’s own person, to live one’s life, and not to be the product of manipulative or distorting external forces [43]. In liberalism, autonomy implies the ability to reflect wholly on oneself, to accept or reject personal values, connections, and features [43].

Privacy is a vague, emotional, and contextual concept with social and technical dimensions [44]. It is dynamic because people’s privacy expectations and needs vary situationally. Privacy is also one of the human rights. The UNDHR expresses that “No one shall be subject to arbitrary interference with his privacy” [35]. Autonomy and privacy are closely interconnected. According to Citron [45], without personal autonomy, there is hardly personal privacy, and privacy ensures dignity. Furthermore, we must understand privacy as a moral right, human right, and legal right, and that invasion of privacy is a violation of human dignity [45]. Because privacy is not an absolute right, it is not uncommon to think that other values, such as the public good or common good, can supersede it.

Traditionally, the focus of privacy has been the individual, the individual’s space, and individual interests. It has been seen as a right to control what others know of us, and the ability to control data collection, use, and disclosure [44]. Control of data flow and use of data are dominating mechanisms to balance others’ “need to know” and individuals’ intention to limit what others know of us [39]. Contextual privacy by Nissenbaum [46] is a new approach that allows information to flow freely inside the context and between contexts following “filtering rules” defined by stakeholders in the context. Privacy also has a social dimension, enabling the management of the social boundaries and freedom of thought, including limiting the power of governments and data companies regarding the use of data [45,47].

According to Wang et al [48], digital privacy is a “selective psychological and technical control of access to the digital self in the form of online profiles, personal data, and digital assets.” Internet privacy addresses the level of privacy a person has on the internet. Online privacy refers to the right to privacy in online situations. New ideas about privacy include group privacy, privacy as ability, privacy as property, and mental privacy. The authors state that privacy remains an abstract concept until people have the ability and power to express personal privacy preferences in real-life situations.

According to Abernethy et al [11], the prevailing pervasive, dynamic, and virtual digital world, where passive and online data collection on people’s activities and behaviors takes place routinely, and where people self-disclose their personal information, has challenged most of the traditional privacy models. The digital world is virtually without privacy boundaries, and information collection, sharing, and self-disclosure make us transparent to others. This and AI transform how privacy is understood and change the level of actual privacy we have [49]. According to researchers, in the digital world, privacy is not only needed to protect personal health-related data but also to preserve one’s human rights, dignity, and autonomy [39,48,50].

Different countries have national regulatory frameworks for health care regulating the collection, use, sharing, and storing of patient data and patients’ information autonomy. Thus far, DH-specific laws have been missing, and therefore, existing general and health care–specific regulations can be used where appropriate. In a former paper [51], the authors stated that the current regulatory environment is insufficient and outdated for digital environments. Brantly and Brantly [52] argued that the regulations fail to place the patient or person and his or her well-being at the center, and the expected benefits of needs of digital markets increasingly override the data subject’s privacy, dignity, and autonomy [52].

The power and benefits of DH are strongly dependent on the availability and quality of health data coming from different sources, such as health care activities, smartphones, health apps, and social media, frequently generated by patients themselves [53]. There are different opinions regarding which data should be classified as health data and the nature of health data. Currently, it is unclear whether health data should cover lifestyle and well-being data and sensor data regarding an individual’s daily behaviors, habits, and preferences [54]. A narrow view on health data includes only data generated by health care professionals and medical devices, that is, the content of the regulated patient record. A wider view recommends that all kinds of health-related data, which come from inside and outside the regulated health care domain and can be collected, processed, stored, and interlinked, should be included in the discussed health data space [55,56]. In this wider view, sources of health data include regulated health care service providers, nonregulated wellness firms, governments, internet firms, social networks, industry and researchers, and people and patients who voluntarily generate and disclose health-related data.

A similar dichotomy also exists in regulations. The European Union General Data Protection Regulation (EU-GDPR) treats health data as a special category of sensitive information and uses a broad view of health data by defining it as “personal data concerning health” that includes “all data pertaining to the health status of a data subject” [57]. Recital 35 of the EU-GDPR details this definition by defining health data as “any information on a disease, disability, disease risk, medical history, clinical treatment, or the physiological or biomedical state of the data subject” [58]. In Contrast, in the United States, the term “protected health information” is used for health data, covering only entities directly related to health care operations [55,59].

Opinions concerning the nature of health data also vary. Health data are understood as public goods, common goods, commodities, or personal properties [60]. Some argue that health data should be common goods that enable economic growth and new research innovations [61], and others propose the open data model as the future way to go. Martin and Begany [62] argued that government agencies should implement an open health data model for better efficiency and for benefits, such as improved health literacy, data-driven changes in health care delivery, consumer engagement, and community development. In contrast, Verhulst [27] stated that health data cannot be treated solely as a public or private good.

The ownership of health data is also a critical question. Opinions are shared regarding the concepts of ownership and property of health data. In principle, the owner of health data can be the data subject or patient, a public or private organization, or the ownership can be shared. Nowadays, companies (eg, internet giants and mediators) that have used remarkable resources to collect, organize, and use health data often expect that “they either fully own or have full and complete rights to the data” [30,47]. This view is also supported by the EU in its regulation that grants property rights to those “who collate large compilations of digital data and other data” [63]. As part of the digital single market strategy, the European Commission sees that ownership, such as property rights and database rights, poses a threat to the development of an EU data market, and big data should be as freely accessible as possible [64].

A benefit of personal ownership of health data is that it gives the person or patient the power to maintain privacy and autonomy in different contexts and situations, and to restrict unnecessary data collection. Shared ownership of health data means that, in a specific context, a group of users have the right to use it freely. According to Lipton [30], the models of legal ownership require human effort to create those data, and because personal information is not created by a person, it cannot be owned. Some researchers have proposed that in the digital world, health data should be a special and legal intellectual property of the data subject [65].

A DH system can be built using different organizational solutions and models, such as public-private partnerships, data trust models, data cooperatives, data collaboratives, and health data market models [66]. DH can be a public-private partnership system or a multistakeholder health ecosystem, where the main players are commercial organizations owning not only health data but also algorithms, analytic tools, and personal health applications supporting self-diagnosing and self-care, and where the role of public health care is small. Data collaboratives are initiatives for data collection, sharing, and processing. The health data cooperative (eg, a health data bank) enables the users to collect, store, manage, use, and share health information to analyze it, and to conduct big data analytics [67]. In the data cooperative, health data are equal properties of all its members, and it offers worldwide accessibility to data [67,68]. The platform for health data marketplaces can be owned by the government or a private organization [69]. Its business model is built around personalization and the commodification of incoming data. Data trusts are intermediaries where the owners or managers have the responsibility to act in the interest of beneficiaries [39]. An interesting new solution is based on the fiduciary duty model [70,71]. According to Hashiguchi et al [70], the legal fiducial duty model can be used in public-private partnership solutions.

From the technological point of view, DH is a complex sociotechnical information system. Different architectural approaches and different hardware and software solutions can be used to realize DH systems, such as a digital ecosystem and platform, blockchain, edge computing, confidential computing, federated learning system, digital algorithms, the Internet of Things devices, smart sensors and wearables, and health applications, among others [26,60].

New technological innovations are coming soon into practice, such as digital phenotyping, biomedicalization, use of digital biomarkers, synthetic biology, gene manufacturing and editing, DNA tailoring and printing, brain computer interface (BCI), use of digital biomarkers, generative AI, AI-enabled robotics, quantum computing, and digital twin technology [72]. They offer new possibilities for DH and in the development of new services and tools, such as online patient monitoring, self-reporting, AI-assisted care robots, AI-based DH assistants, and conversational agents using generative AI [73].

Digital biomarkers are quantifiable physiological and behavioral data collected by personal digital devices, and digital phenotyping involves the collection and quantification of individual-level characteristics. Both are used to explain, influence, and predict health-related outcomes [74]. The focus in biomedicalization is behavioral and lifestyle modifications of humans [75,76].

BCI uses implanted or outside-the-body monitoring devices to measure, quantify, and interpret human behaviors and even thinking [77]. In BCI, direct communication link between the brain and a computer is used to enable access to our mind and even to upload our brain with new data [78]. Currently, BCI allows recording of brain activity and translation of brain signals to text [79].

Th digital self is a virtual model of a person’s physical characteristics and provides a larger view of an individual as a computer representation in the digital domain based on their personal data [80]. It can be used to simulate a person’s behaviors [81]. A digital patient twin (DPT) is a simulation model of a patient constructed from patient data, genetic data, personally generated health data, data from monitoring devices, and population data [81]. Generative AI, digital phenotyping, and digitalization of the human body and behaviors enable the creation of the DPT. It offers huge opportunities for “precision medicine” by enabling the simulation of how the body responds to treatment and medication.

The digitally engaged patient (ie, the empowered or activated patient) approach assumes that more information leads to improved quality of care and economic efficiency. Digitally engaged patients use personal monitoring tools to self-monitor personal biomarkers, self-care of their own illnesses, and manage their personal health [4]. It is expected that in the long run, digitally engaged patients can develop health care provider–level expertise concerning their diseases.

Because new technologies offer unprecedented, detailed insight into the human body and mind, they also raise new concerns for human rights, privacy, dignity, and autonomy [23,82].

The ultimate goal of the HCDH model is to support good health and wellness for all and not to produce harm and maximize profit. It sees DH as a sociotechnical system whose core elements are ideas, goals, principles, and regulations. The term “human-centered” means that the patient’s or person’s needs for human rights, dignity, autonomy, and privacy are acknowledged, and the person or patient is the stakeholder in the DH system, having the power to use autonomy to set personal privacy obligations. The model highlights that the main challenge in creating HCDH are the formulation of fundamental ideas and goals, understanding health data in the right way, and the creation of principles, new laws, and requirements. System thinking and modeling methods were used to develop the model. The model uses broad definition for DH provided by Fatehi et al [18] and takes a wide approach to health data, as depicted in Figure 2.

In this model, ideas, goals, and concerns affect the formulation of principles. They also affect how health data are understood and managed by laws and regulations. Laws that regulate DH systems need a huge amount of health data. The role of technology, architecture, and innovations is to enable the realization of the HCDH system.

Foundational ideas and goals for HCDH are derived from the results of the literature analysis and concerns discussed in the DH: Concerns to Human Rights, Dignity, Privacy, and Autonomy section are shown in Table 2, together with corresponding ideas and goals, ideas of economic markets, and neoliberalism.

For designing and managing HCDH ecosystems, we must understand and formally as well as consistently represent multidisciplinary and dynamic systems in various contexts for enabling mapping between the different technologies, disciplines, methodologies, perspectives, intentions, and languages, among others. The solution is a system-oriented, architecture-centric, ontology-based, and policy-driven approach to transformed health ecosystems, using the universal type theory for abstraction and universal logic for representing relations. ISO 23903:2021 “Health informatics—interoperability and integration reference architecture—model and framework” provides a generic specification for managing interoperability and integration challenges in dynamic, complex, context-aware, and multidisciplinary health systems as “system of systems.” The system is represented using the generic component model [107,108]. A model is thereby defined as a representation of objects of a domain, their properties, relations, and interactions, enabling rational and active business in the represented domain (Figure 3). It consists of 3 dimensions: the domains involved in the business process, the granularity (composition and decomposition) of the system components, and the view of the evolutionary or development process. Thereby, the latter follows the ISO 10746 Reference Model Open Distributed Processing, defining the views of enterprise, information, computation, engineering, and technology; however, it is inevitably extended by the real-world business view.

The domain actors define the goals and objectives of the ecosystem. The behavior of the system is controlled by the policy domain, which must be refined into the following policy subdomains:

The policies are formally represented using the policy ontology defined by the authors in ISO 22600 Privilege Management and Access Control.

Figure 3 formally represents all aspects shown in Figure 1, such as technology as domain information; communication technology, digital technologies, policies, information, data, implementation, and maintenance as viewpoints; as well as ideology, motivation, and beliefs as contexts.

On the one hand, the expected positive impacts of DH seem to be so remarkable that the ongoing transformation is justifiable. On the other hand, DH should not overshadow human rights and cause the loss of dignity, privacy, and autonomy. In this paper, we have analyzed ideas and principles driving the evolution toward DH, and the impacts of this transformation on human rights, dignity, privacy, and autonomy through 6 views. On the basis of an extended literature analysis, we found that 1 possible transformation to DH uses ideologies of digital market economy and neoliberalism. Our analysis show that this kind of development, linked to the belief that technology has only 1 predefined direction, raises the risk of degradation of human rights, loss of dignity, privacy, and autonomy, and increasing inequality in health. In this paper, an alternative solution called “HCDH” is proposed. It respects human rights and dignity and maintains privacy and autonomy. In this model, a person or patient is a stakeholder, and not just a source of raw material or consumer. For HCDH, we have developed a system model; incorporated human-centric ideas, principles, and requirements; and proposed supporting laws. Cornerstones in HCDH are new rights that offer an individual a high level of autonomy and privacy, and a special kind of health data ownership, where health data are personal properties and restricted public goods. Our solution is also proactively prepared for privacy and autonomy concerns arising from new technological innovations, such as DPT and the use of AI for behavioral manipulation.

The HCDH model also has challenges. A major challenge is that guiding rules and principles of HCDH limit the ability of the data industry to collect, use, privatize, and monetize health data and use it for inferred or derived digital products and applications. As the current industry’s business model is based on free collection and use of data, they will voluntarily not stop the extensive collection of health data, push back its “ownership” to data patients and persons, and will not quit their money-making machine, thereby reducing their economic growth and ability to make innovations. Another problem is the wide definition used for health data. In the future, the findings of medical research and the better understanding of determinants for health can lead to an undesirable situation where most personal data would be health-relevant and also part of health data [55,61,109]. The industry may also argue that the expected benefits of the collection and use of health data legitimately pose possible negative consequences to human rights, dignity, autonomy, and privacy, and those consequences can be solved later [78]. The authors see this differently. In most real-life cases, innovations and development of new health products and services can be realized using AI-generated artificial data, and the current extensive, and wide collection of health data that violates human rights, privacy, and autonomy is unnecessary. Furthermore, the idea that negative consequences can be solved in the future is only a belief.

A possible barrier is that HCDH will be discarded by policy makers and data industry. In this case, the only protection persisting for the data subject or patient is to self-defend privacy, dignity, and autonomy at a local or personal level. There are already solutions for this approach, such as the creation of virtual unlinked personalities; hiding sources of data; developing apps, which enable the monitoring and filtering of data flow from personal devices; using personal intelligent agents to make data vanish; and using applications that generate poisoned information for data collectors and for AI training bases. However, the authors argue that wide range use of self-defending tools will have major negative impacts on the usability, quality, and reliability of health data repositories and it can distort results of AI applications [110]. Therefore, this kind of development is undesirable and should be avoided. Instead, governments and industry should offer people solutions that proactively prevent the misuse of health data by promoting human rights, dignity, privacy, and autonomy [70], such as the proposed HCDH.

DH should benefit people and patients in a way that is ethical, safe, secure, reliable, equitable, and sustainable. We must understand that if human rights, privacy, dignity, autonomy, and personal ownership of health data are lost, we will never get them back. Similar to Wong [39], we state that it is essential to understand that human rights, dignity, privacy, and autonomy should be core elements in DH. Without this, we are in danger of becoming holograms and digital slaves.