Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Ver. Summary Created by Modification Content Size Created at Operation
1 -- 6735 2023-06-29 08:33:33 |
2 format Meta information modification 6735 2023-06-30 07:30:17 | |
3 format -1 word(s) 6734 2023-07-07 10:31:35 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Păvăloaia, V.; Necula, S. Artificial Intelligence as a Disruptive Technology. Encyclopedia. Available online: (accessed on 11 December 2023).
Păvăloaia V, Necula S. Artificial Intelligence as a Disruptive Technology. Encyclopedia. Available at: Accessed December 11, 2023.
Păvăloaia, Vasile-Daniel, Sabina-Cristiana Necula. "Artificial Intelligence as a Disruptive Technology" Encyclopedia, (accessed December 11, 2023).
Păvăloaia, V., & Necula, S.(2023, June 29). Artificial Intelligence as a Disruptive Technology. In Encyclopedia.
Păvăloaia, Vasile-Daniel and Sabina-Cristiana Necula. "Artificial Intelligence as a Disruptive Technology." Encyclopedia. Web. 29 June, 2023.
Artificial Intelligence as a Disruptive Technology

The greatest technological changes in our lives are predicted to be brought about by Artificial Intelligence (AI). Together with the Internet of Things (IoT), blockchain, and several others, AI is considered to be the most disruptive technology, and has impacted numerous sectors, such as healthcare (medicine), business, agriculture, education, and urban development.

artificial intelligence disruptive technology disruptive innovation

1. Introduction

Since the advent of the concept of disruptive technology, coined by [1][2] and completed by [3], and until now, its meaning has undergone an evolution that has somewhat altered its initial connotation. The specialized literature uses disruptive innovation as a synonym for disruptive technology, and it refers to the disruptive effects of new technologies within a domain.
As a result, historically speaking, disruptive technologies provide entirely new bundles of characteristics that the general public is hesitant to employ in the applications they are accustomed to. According to [1], disruptive technologies are introduced and bring value only for new applications and new markets since they stimulate the development of new products and markets.
In his book [3], Christensen distinguished between two categories of technologies that have a great impact on organizations: sustaining technologies and disruptive technologies. In this way, sustaining technologies were regarded as those that would complement and benefit from those that are currently in use. Conversely, disruptive technologies are those that have recently emerged and have the potential to have an unexpected impact on already-existing technologies. They were thus seen as unrefined, underperforming, and lacking in practical ability [4], leading to their poor reputation.
In the meantime, this perception (which was adverse) towards disruptive technologies was quickly enforced in the eyes of specialists. Analysis of the specialized literature supports the disruptive nature of new technologies. Thus, disruptive technologies that are initially tested on niche or small markets may later become more competitive in the mainstream market, thereby displacing products that are based on proven technologies. This is because a product’s level of technological advancement frequently exceeds the rate at which customers typically want or can absorb performance improvements. Furthermore, products whose features and functionalities meet today’s requirements will eventually undergo an improvement process that will meet tomorrow’s dynamic needs. Whereas products that currently perform very poorly in comparison with consumer expectations for the primary components may perform very well in the future and become directly competitive.
Among the recent perceptions of the term “disruptive,” the authors of [5] contend that these technologies are termed disruptive because they substantially modify the usual way of operating, without bearing any negative implications. In the same stream of ideas, Ref. [6] considers that disruptive technologies are termed as such due to their radical computational power, near-endless quantities of data, and unprecedented technological advances. Others [7] contend that they can cause broader societal transformation by changing the existing economic sectors, working principles, manufacturing characteristics, and consumption behaviors because they have the potential to disrupt the status quo throughout developing a unique set of values.
The topic approached in this manuscript is aligned with the ISO-revealed trends until 2030. Thus, during World Standards Day in 2018, an ISO article [8] stressed that disruptive technologies such as AI, robots, nanotechnology, and the IoT are the hallmarks of the Fourth Industrial Revolution (4IR). Furthermore, in 2019, the ISO [9] elaborated on the four trends that make up the disruptive forces shaping the direction of ISO’s future strategy in the period leading up to 2030. Among them is enlisted digital transformation, where AI (through ML) and blockchain supply organizations with a broad range of options, are enhancing their productivity and efficiency while fostering innovation and competitive edge.

2. AI as a Disruptive Technology in Healthcare (Medicine)

The study of the specialized literature emphasizes the applications of AI technologies as the center for disruptive innovation in many areas and professions within the field of healthcare and medicine. Thus, AI impacts surgeons and surgery [10], advanced medical imaging [11], dietetics and dietitians [12], and radiology and the early stages of cancer detection, while [13] builds on dental and oral medicine with a positive impact on dentistry practitioners by using Robotics [14].
One direction regarding AI as a form of disruptive innovation is emphasized by [12]. As such, the study of the medical literature uncovers many instances of customary human tasks being computerized [14] through the adoption and implementation of AI and DL. Integrated electronic and personal health records, mobile apps, wearables, AI and ML, conversational interfaces such as chatbots, and social robots are some examples of such disruptive technologies. The authors of [12] describe how the traditional model of nutritional care delivery is being disrupted by digital health, as well as the opportunity for dietitians to embrace this disruption and take ownership of it in order to improve patient care. According to the four elements of the nutritional care process, namely, nutritional assessment, diagnosis, intervention, and monitoring and evaluation, the article provides an overview of digital health concepts and disruptive technologies.
Although AI is by far the disruptive technology that has had the most important impact in medicine, several other technologies could be combined for better results. Blockchain with AI can be used for corroboration and correlation with ML algorithms applied to managing a patient’s history with all their medical records [11]. Further [15], AI (using ML algorithms) might be executed every time a new imaging study is added to the blockchain thanks to smart contracts, thus simulating real-time analysis and augmentation [12].

2.1. Disruptive Features in the Applications to Surgery

AI has strong disruptive features with respect to its applications to surgery [10], a subdomain of medicine, due to its positive impacts recorded in guided surgery and regarding advanced imaging [11]. Given the availability of endoscopic recordings that can naturally guide such treatments, only minimally invasive surgery has been performed so far in this respect [11], and most of the focus thus far has concerned the use of AI for intraoperative assistance. Ref. [16] argues that comprehensive spinal care could be revolutionized by AI. As such, personalized postoperative care, real-time surgical indications, and preoperative patient selection can all be improved for surgeons through the aid of evidence-based and predictive analytics. Although it is still in the early phases of development, robotic-assisted surgery has the potential to increase technical accuracy while decreasing surgeon fatigue. AI-based robots can analyze data from previous surgical procedures to develop new surgical methods. These robots can perform surgery more accurately with reduced accidental movements. Apart from spinal surgery, AI also finds applications in minimally invasive surgery, surgeries assisted by robots, and post-surgery care, such as calculating recovery time. Ref. [17] focuses on the advantages of robotic surgery and draws attention to the fact that there are no such applications in vascular surgery. The authors highlight the positive benefits of disruptive technologies, such as AR/VR for surgery and 3D printing for high-fidelity surgical templates, in the context of the digital revolution of surgery (surgery 4.0).
Robotics is a revolutionary field that will transform dental medicine’s diagnostic and therapeutic procedures [13]. The most recent medical dentist robots can conduct patient interventions or remote monitoring independently since robotic systems have evolved dramatically over the past ten years [14]. The authors performed a systematic literature review and extracted the areas of dental medicine where robots are used intensively. The following domains were determined to be very prolific with respect to the implementation of robots [14]: dental implantology, oral and maxillofacial surgery, prosthetic and restorative dentistry, orthodontics, oral radiology, dental hygiene applications, dental assistance, and the production of dental materials. Undoubtedly, robotic dentistry has a disruptive rather than destructive impact.

2.2. Disruptive Features in the Applications to Healthcare

Since the early stages of their conceptualization, robots have been designed to replace humans in sectors that endanger human life. The effective employment of robots in healthcare was shown during the COVID-19 pandemic. Thus, to reduce the risk of human contamination and illness in high-tech, developed countries, robot technology was used intensively for various applications [18], including the distribution of food and medicine to ill persons, the provision of assistance to elderly people and those with disabilities, and biopsy extraction (with endoscopy bots) to test for diseases (anemia, bleeding, inflammation, diarrhea, or cancers of the digestive system).
Ref. [18] also brings to attention several other disruptive technologies, such as 3D printing. In this regard, the author emphasizes that 3D printers fabricate low-cost prosthetics where people need them and in a cost-efficient manner. In strict connection with COVID-19 [19], appeals have been made regarding the positive effects of using disruptive technologies (such as blockchain and AI) to address the problems posed by the pandemic.
In [19], the authors examine the potential uses and applications of blockchain and AI in the context of digital healthcare, which makes use of the increased accessibility of health data to identify high-risk patients, monitor the spread of infections, forecast mortality risk, manage healthcare data, and combat COVID-19 and other pandemics. Furthermore, Ref. [20] draws on the features of explainable AI (ExAI) by elaborating on the opportunities and challenges in the context of Healthcare 5.0. The study demonstrates the effectiveness of ExAI in healthcare environments that include real-life model deployments in a variety of clinical applications.
Since AI improves treatment outcomes, lowers medical errors, and aids diagnosis, it makes it simpler for medical practitioners to care for a larger number of patients [19][21]. Although this technology can help address HR difficulties, such as finding and vetting potential healthcare workers, AI cannot cover the entire spectrum of care (e.g., providing empathy); thus, the human touch and communication are still essential. Although personal connections and trust cannot be replaced by a program, device, or application, the authors contend that AI has great potential as a cognitive assistant [21].
Some authors [22] have examined the larger context of AI and ML liability and how it affects the safe application and innovation of new technologies in clinical care. As a negative aspect of disruptive technologies, these authors broach the issue of algorithm inaccuracy, which can result in poor clinical judgment and unfavorable patient outcomes. These mistakes give rise to worries about patient damage responsibility and may clarify what makes it difficult to implement AI and ML in clinical practice.
The encouragement of the use of disruptive technologies in the clinical laboratory is necessary for a number of reasons, including the rising expenses of health care, the need for improved accessibility to diagnostic care, and the growing need for laboratories in the era of precision diagnostics. [23]. When combined with other medical data, such as clinical biology and genetic data, these technologies will significantly alter how the medical system is structured and organized [24][25].
Although the many ethical questions and challenges that arise from the use of AI in medicine are not covered by this research, they cannot be ignored. AI has the potential to advance medical practice in the future, but there are [26] numerous ethical and legal issues associated with its use in the healthcare industry [24]. The legal and ethical debates [27] surrounding AI in medicine involve many different parties [10]; additionally, there is considerable reluctance towards disruptive advances expressed by current technology suppliers and governmental regulatory authorities [23]. To mitigate the legal and ethical issues related to AI in healthcare, a multidimensional approach encompassing legislators, developers, healthcare practitioners, and patients is essential.
Finally, the here argues that the development of three radical disruptive innovations—namely, the digitalization of medical imaging techniques to enable their parametric use (1), the development of algorithms to enable the use of NLP on medical records (2), and the development of DL algorithms to treat uncategorized data (3)—led to the emergence of AI in the medicine and the healthcare system. As a result of these disruptive technologies’ greater accuracy compared to that offered by radiologists, they can already automatically detect lesions and pave the path for the identification of different types of cancer [24].

3. AI as a Disruptive Technology in Business—Logistics and Transportation and the Labor Market

Blockchain and AI are currently two of the most popular and disruptive technologies [28] across all industries, particularly business. The disruptive technologies that are driving the digital revolution [29] may assist businesses in terms of solving complex problems and improving consumer value across all business areas.
On the one hand, blockchain technology enables decentralized, secure, and trustworthy access to a shared ledger of data, transactions, and records. Additionally, this technology enables the implementation of smart contracts to control participant interactions without the use of a middleman or trustworthy third party.
The subdomains of business where AI manifests its disruptive features are logistics and transportation and the labor force.

3.1. Logistics

The current state of the logistics industry is set to be disrupted [30][31] by the fourth industrial revolution (4IR), but there are also opportunities that can be seized in order to exploit disruptive technologies to develop new business models while retaining the current ones. This pattern has led to an increase in the number of stakeholders who are concerned about how disruptive and edge technologies will impact freight transportation and how decision-making in logistics management will be enhanced. The literature elaborates on the major emerging technologies in freight transportation (T) and logistics (L) and presents research wherein disruptive technology brings numerous benefits to L. For example, Ref. [32] argues that disruptive technologies (especially blockchain) enhance the sustainability and resilience [33] of L, while [34] refers to green L (green distribution, reverse L, and green warehousing). Moreover, due to the COVID-19 pandemic, Ref. [33] emphasize the acceleration of the digitalization trend in L based on the adoption of disruptive technologies focusing on blockchain, the IoT, data, drones, robots, and autonomous vehicles.
By combining the IoT, smart robotics, and digital twins, Ref. [35] further details one of the disruptive effects of Industry 4.0/5.0 known as reverse L. This information might aid remanufacturing enterprises with respect to making a smart and easy transition to the new industrial era. Another original study [36] builds a framework for identifying disruptive technologies and chooses the intelligent logistics robot technology to conduct an empirical examination in the AIoT sector. The study shows that efficient, intelligent control operations; positive human–computer interactions; the precise avoidance of safety obstacles; and efficient and accurate location detection are the future development paths of intelligent logistics robots.
Ref. [31] highlight the demand for technology adoption and process digitalization in Logistics among Logistic Service Providers (LSP). According to the authors, disruptive technologies, particularly blockchain, the IoT, and Bigdata, have the potential to expand the boundaries of supply chains’ traceability, transparency, accuracy, and safety.

3.2. Labor Market

The specialists warn that in the upcoming decades, disruptive technologies such as AI will have a significant influence on the workplace by widening the work force’s skill gaps more quickly than educational systems can adjust [37]. Thus, on the one hand, educational systems should adapt their curricula to incorporate edge technologies. On the other hand, employers should invest in training courses to raise awareness among their employees with regard to disruptive technologies and their impacts on the workforce.
As new jobs are being created, new skills should be developed. Ref. [38] predicts that in the 4IR, disruptive technologies (AI, robots, and algorithms) will replace 1/3 of the existing jobs. At the same time, because some jobs are being assumed by technology, employees should be retrained and diverted to other jobs. The involvement of academic institutions is vital in the case described above, and employers should collaborate with universities and keep them updated regarding technological changes that affect business.
According to some authors [37], social innovations and inclusiveness can be used as tactics to lessen the effects of disruptive technologies, which are expected to occur increasingly often.
To create and use AI [39], specific technical expertise is needed, which is a certain sign that the number of technical jobs is rising. However, this particular demand represents a significant barrier in terms of skill acquisition and the employability of middle management, senior workers, and all of an organization’s human resources (HR) staff. One study [39] concentrated on the introduction of AI-based technologies into an organization as well as the new potential and challenges with respect to managing HR while accounting for both technical and nontechnical resources inside businesses.
Through the numerous technologies that derive from AI (NLP, ML, Reasoning, and Computer vision), AI is disrupting the workforce arena. In order to render computers intelligent [39], researchers involved in the development of AI are currently seeking to give machines characteristics resembling those of the human brain. There are several instances of this process that can be observed, including in terms of speech recognition technology, robots, digital customer service agents, and personal digital assistants. By employing algorithms and programming, it is possible to teach machines to exhibit traits like those of humans, including knowledge acquisition, problem solving, learning, perception, planning, manipulation, and others.
Robotic Process Automation (RPA) is another example of disruptive technology that impacts the workforce [33]. RPA is replacing human laborers; this is not because it is smarter, but because it is less expensive, more readily available, and less physically demanding than human laborers.
With regard to managing and enhancing organizational growth, technology and personnel perform optimally when used jointly [38]. The sole condition for humans to compete with and outperform AI is for them to improve their existing talents and show a desire to learn new ones based on knowledge.
AI (ML) has the ability to influence [40] organizational strategy, management procedures, and customer behavior, producing virtually infinite volumes of data (big data). Companies must radically adapt to new ideas and technology, such as the IoT, big data, ML, AI, and others. Additionally, they must establish systems (frameworks) for the ongoing examination of the potential advantages and challenges posed by disruptive technologies.

4. AI as a Disruptive Technology in Agriculture

In the literature, the trends in agriculture and adjacent domains that are most influenced by the AI’s disruptive properties are smart farming, 4IR, agriculture 4.0, and digital twins.
The relevant papers included in analysis reveal that disruptive technologies can contribute immensely [41] to agriculture. Some of the disruptive technologies identified as contributory to agriculture include the IoT, smart devices, and a multitude of AI techniques such as ML [41][42], Image Recognition [43], Modelling and Simulation, and Data Analytics [41]. According to [44], the emergence of disruptive technology such as artificial intelligence has a significant impact on raising agricultural outputs.
Some authors list technology as one of the factors that contribute to the creation of forms of wealth such as natural resources, capital, labor, and others. More specifically, AI, the IoT, and blockchain technology have been identified as commonly used disruptive and innovative technologies in many domains, including agriculture. In Asia, more specifically in the Philippines, Japan, and China, Ref. [43] contend that although AI is among the most controversial technologies to date, it has benefited many areas, including agriculture. Moreover, Ref. [43] highlights that Asian countries have estimated that their future AI market will grow (until 2030 or 2035), namely, by 23.51% in Japan and 40% in Singapore, and that this will transform China into a world leader. Furthermore, in the Philippines, the investments in AI will accelerate the innovation rate by 1.7% while doubling employees’ productivity rates [45]. These achievements are due to governmental involvement in crafting a national AI road map to which many Philippines’s Departments adhere, including the department of agriculture. This strategy aims to place the Philippines as an AI powerhouse in the Asian region, and it can be considered a best practice with world-wide applications.
AI, as a disruptive technology, is very frequently associated with the concept of smart farming (and digital twins).

4.1. Smart Farming

In terms of software development for smart agriculture and smart farming, world-leading companies such as Microsoft have implemented [45] AI techniques on agriculture via a mobile application (Krops) for the Philippines’ agricultural department. The above-mentioned Azure-based mobile platform helps local farmers optimize their profits from yield by disrupting the old buying and selling practices, where small networks of buyers control prices and access to markets. Furthermore, in the United States [43], the farming sector is benefitting from AI technology’s innovation of the aquaponics market [46]. Consequently, the same technology supports households’ ability to save water through smart apps such as Skydrop that integrates weather forecasting with smart irrigation.
The constraints and potential future steps for modeling and modelers in the animal sciences are being considered by a group of researchers [47] examining the disruptive innovations in animal farming. The authors conducted an analysis and emphasized how the identified models, supported by AI, might provide a superior and long-lasting function in the field of animal sciences. They concluded their research by making suggestions for how future animal scientists might support themselves, farmers, and their discipline while considering the benefits and difficulties presented by technological progress.
Another disruptive feature of AI is emphasized by [43] through an image recognition technique that enabled users to browse through more than 50,000 plant images to help identify crop diseases at certain sites. In addition, Ref. [48] elaborate on the use of satellite or drone image analysis to provide vigor and water stress indices or even trigger alerts for pests and diseases. Such apps are available for mobile devices, have a success rate close to 100%, and assist or even replace specialists in the fields in question.

4.2. Digital Twins

The concept of digital twins is considered a disruptive technology [49] that has revolutionized the industrial world, particularly the manufacturing industry, the construction and healthcare sectors, smart cities, the energy industry, agriculture, and modern animal farming.
The concept of digital twins denotes the development of a digital copy for a real entity in which the physical and biological states and behaviors of the entity are simulated based on a set of input data. Some authors [42][49] have revealed the real world applications of this concept in many areas including agriculture and animal farming. The use of digital twins in the livestock-farming industry represents the next frontier and has the potential to enhance the utilization of technology and equipment, large-scale precision livestock-farming methods, and the health and welfare of a range of farm animals. Using AI-based recognition technology that analyzes facial traits such as ear positions and the white regions of the eye, it is possible to keep track of the mental and emotional states of animals. Digital twins, through the usage of modeling, simulation, and AR technologies, can help farmers construct more energy-efficient buildings, predict heat cycles for breeding, discourage undesirable animal habits, and possibly much more. The adoption of digital twin technology will necessitate a detailed cost–benefit analysis of each farm, as is the case with any disruptive technical advancement.
The benefits of digital twins in relation to the disruptive effects of technology consist of the ability to predict, optimize, and improve the decision-making process.

4.3. The Fourth Industrial Revolution (4IR)

The 4IR affects every agricultural player regardless of their size (whether family subsistence farmers or massive producers) in terms of food manufacturing and related goods. Under the impact of technology and globalization, worldwide agriculture players use technological tools based on AI, blockchain, and the IoT for profit maximization and business strategy improvement. In the context of the 4IR, which impacts many industries including agriculture, the extant literature mentions a new concept termed AgriTech. Some authors [50] have elaborated on this concept, even developing a taxonomy of its various types, and identified the AI-driven techniques that form the continuously shifting definition of AgriTech.
Very few researchers have [51] tackled the role of blockchain with respect to operations traceability in areas such as e-commerce, agriculture, public services, etc. In this respect, [52] aimed to extract and determine the relationships between the enablers of blockchain adoption in Agriculture Supply Chains (ASCs). The results were intended to help practitioners design strategies for blockchain’s implementation in agriculture by creating a real-time, data-driven ASC by blending the IoT, AI, and 3D printing disruptive technologies.
Ref. [48] brings to attention the adoption of disruptive technologies, such as AI and blockchain, by start-ups, SMEs, and other companies for developing smart farming, precision and urban farming, and data management to reduce waste in order to redefine their business models.
Among its positive effects, Ref. [49] state that AI can also lead to an increase in the employment rate in all economic sectors if governments implement a proper talent-training strategy. Another benefit brought by disruptive innovation in agriculture consists of the maximization of production [44]. Thus, farmers now use precision farming, which employs AI techniques to monitor crop health, detect weeds, identify and detect plant diseases, and forecast weather and commodity pricing. Since there is a lack of labor in the agricultural industry, AI-based tools such as bots and drones are frequently deployed.
Consequently, in order to counteract the negative effects brought by AI as a disruptive technology, a dissemination of AI’s positive benefits for humankind should be undertaken. Although humans are superior in terms of creativity and imagination, AI can enhance their abilities by assisting them in processes that involve data analytics and the use of precise and advanced algorithms. In addition, in the areas where human health can be endangered, robots can replace them to save lives.

5. AI as a Disruptive Technology in Education

The modern education system is founded on intelligent learning environments and competence-based learning, and it uses a wide variety of platforms that rely on AI technologies in the education process [6][53][54]. More than ever, engineering education, and specifically fields such as computer science and engineering, must adjust to the changes brought about by cutting-edge technology. The necessity for skill-oriented, project-based learning over traditional engineering education that places a strong emphasis on theoretical notions is explored and emphasized by the authors of [55]. Within an integrated and unified educational system, novel methods for engineering education are already offering [56] innovative, technology-enhanced, individualized, student-centered curricular experiences. All industries are predicted to experience an increase in talent and skill shortages in the upcoming years, yet disruptive technology can also help to close these gaps.
The keyword for this new stage of education is education 4.0 [6][57], which involves the use of disruptive technologies [58] such as AI, robotics, blockchain, 3D printing, 5G, IoT, digital twins, and augmented reality. Moreover, Ref. [7] links education 4.0 with Industry 4.0 by urging governments and universities to step-up and adopt Education 4.0 to produce skills for the workforce of industry 4.0. In this endeavor, they conducted an electronic survey and contributed [7] by identifying 35 disruptive technologies, among which 13 were quantified as key technologies: the IoT, big data, 3D printing, cloud computing, autonomous robots, Virtual Reality (VR) and AR, cyber-physical systems, AI, smart sensors, simulation, nanotechnology, drones, and biotechnology. They emphasize the urge to link education (through curricula adjustments) to the above key technologies with Industry 4.0 requirements.
While AI and blockchain are expected [6] to be the most disruptive classes of technologies over the next 10 years due to the development of radical computational power, near-endless quantities of data, and unprecedented advances in deep neural networks, they can be used to improve the methods and tools in the learning process. Furthermore, the authors identified in this investigated the relationships between these disruptive technologies and education and revealed that blockchain can be used for the automatic validation and transfer of academic credits, the storage of students’ learning materials (either formal and non-formal, such as learning at the workplace), and the verification of the authenticity of documents. Ref. [6] also mention the use of blockchain to pay for courses using cryptocurrencies and allow for student identification using biometric identification on smartphones. Alternatively, AI can be used for education management and delivery, learning and assessment, empowering teachers and facilitating teaching, dialogue-based tutoring systems, and providing lifelong learning possibilities.
Some of the educational activities that involve AI in the management of academic organizations include admissions, timetables, attendance, and homework monitoring [59]. Moreover, AI may contribute to selecting relevant learning content across learning platforms for each student in accordance with each individual’s personalized needs [60].
Some [53] refer to the disruptive character of data analytics in accounting as an education field of study. They consider that data analytics redefines the business processes and that the response of academia must be immediate. The authors point out that although data analytics has gained attention at the college level, it has “received little or no coverage in accounting curricula”.
The use of AI as a disruptive technology is strongly impacting sports, both as an activity and a field of study. Some argue that AI, together with its companion technologies (robotics, enhanced vision, and AR/VR), impacts the field of sports as an education discipline, highlighting certain ethical issues [5]. The previously cited author argues that the disruptive aspects of AI impact sports in terms of four constitutive elements, namely, athletes, coaches, judges, and fans, wherein each impact has positive and negative connotations.
Ref. [61] contributed original research to the literature by extracting 12 internal and 10 external success factors for smart professional disruptors in university. The authors also bring into discussion new disruptive technologies that impact education, such as mixed reality (MR), extended reality (XR), and the internet of behaviors (IoB).
The disruptive technologies used in education and their impacts are as follows:
AI (through VR and AR) has been used in education since the 1990s to teach subjects such as mathematics, geometry, physics, chemistry, and anatomy [7];
AI is a technology that augments human cognition in education [62][63];
The IoT [64] is crucial for improving the caliber of educational experiences and student performance, alongside assisting instructors in their everyday tasks, managing school facilities, managing student transportation, and offering remote-learning opportunities.
In education, robotic systems can also be useful. Before students operate on actual patients, dental students can be trained using full-body robots, haptic interface technologies, and sophisticated simulations [14][65].
Ref. [56] argue that the advent of the 4IR, boosted by AI, justifies a paradigm shift in engineering education. All industrial sectors are subject to disruptive change, which increases uncertainty and hampers the anticipation of the future. AI will enable efficient, individualized student learning in this disruptive environment, which will be crucial for future academic achievement. In this context, teachers will teach and program AI in addition to serving as social facilitators.
In conclusion, whilst an impressive number of manuscripts presented the contributions of AI (and other) technologies to the field of education, very few mentioned its disruptive character. Therefore, some side effects of using disruptive technologies (mostly AI) in education consist of: the replacement of face-to-face communication with robots (software or physical), the transformative effect of augmenting human cognition in learning [62][63], the robotization of evaluation and grading processes, and the computerized surveillance of student’s attendance using cameras equipped with face recognition. From students’ perspective, these negative effects may lead to a high course dropout rate, the development of fraud mechanisms, and, ultimately, the loss of the main qualities of education, which consist of building student’s knowledge and cultivating their appetite for various disciplines.

6. AI as a Disruptive Technology with Respect to Urban Development—Society, Smart Cities, and Smart Government

Among the disruptive technologies applied in urban development, the literature mentions the IoT, image processing, AI, big data, and smartphone apps [66][67][68]. These technologies may be coordinated and seamlessly integrated to enhance [67] both the infrastructure for urban growth and the overall wellness of citizens living in smart cities. The term “smart city” has recently been used to describe the integration of disruptive technologies into urban settings to improve citizen experiences. Due to the persistent scientific study that has been conducted in this field over the past ten years, the concept of the smart city has expanded and grown more complex since it was originally defined at the dawn of the twenty-first century. Leading researchers, academics, and industry professionals gathered in 2021 at the International Conference on Sustainable Smart Cities and Territories to exchange knowledge and experiences about the emerging trends and concepts in the transition from smart cities to smart territories [69] as well as real-world smart city implementations.

6.1. Disruptive Technology’s Impact on Society

Since it represents the pinnacle of monitoring and restriction, the development of AI chatbots in some countries offers a fascinating field of research. This is especially crucial now that China has elevated disruptive technologies such as AI and big data as an essential tool for national security and a major element of achieving the country’s dream of national rejuvenation. Several Western fears regarding data security and governmental control have already been fulfilled in China, whether through the introduction of a national social credit system or the widespread use of face recognition technologies. However, it also suggests that China is at the forefront of any possible weak areas and rifts within the party–state–corporate apparatus. The authors of [70] highlight issues regarding the boundaries of humanity in the context of an AI-driven future, while also addressing methodological issues with human–machine interaction while conceiving of new forms of resistance.
The combination of disruptive technologies supports society’s ability to minimize the effects of natural disasters. Accordingly, Ref. [68] advocate for the adoption of blockchain (combined with the IoT) as a permanent record keeper, and cite the Japanese company Zweispace as an example, which uses blockchain to store data from the country’s earthquake sensory data. Zweispace creates proprietary blockchain solutions for the real estate sector as well as real-estate-related apps such as Robot Architect AutoCalc and Namazu for earthquake-resistant measurement. Employing disruptive technologies, the company produced an inheritance smart contract using the Smarter Contract platform, and they began to offer solutions in the financial and construction sectors in order to reshape the future for their clients [71].
Industry 5.0 is one of disruptive technology’s promising future paths [67]. By focusing on human-centered, resilient, and sustainable design, Industry 5.0 will be built on the foundations of Industry 4.0. The conceptualized pathways [67] of Society 5.0, in which a highly integrated cyber and physical platform is constructed, with people playing a prominent role, are another significant development supporting disruptive technology. One study conducted a literature review to explain how disruptive technologies influence many facets of sustainable urban development. The predicted effects of these results’ positive and negative aspects were revealed by the researchers. They emphasize that the digital transformation of several societal sectors—such as healthcare, disaster management, innovative nature-inclusive economic models, the potential future value of disruptive technologies, the transition from Industry 4.0 to Industry 5.0, and the emergence of Society 5.0—are the main ways that disruptive technologies influence the advancement of society. The study stressed that the positive secondary impacts resulting from Industry 5.0 and Society 5.0 initiatives had the greatest overall impact. The reconstruction of industry and society is of the foremost importance from among all the benefits that disruptive technologies may provide, and this establishes the groundwork for subsequent technical advancement.

6.2. Smart Cities

Smart Cities are the outcome of disruptive innovations [72] that harness the technological advances of connectivity, business, sustainability, and government to achieve effective urban development. Urban resilience is a promise [73] included in the concept of the smart city, which broadly refers to a city’s ability to foresee, absorb, react, respond, and restructure in the face of disruptive changes and perturbations. As a result, big data and AI are being hailed as methods or improving and realizing important resilience-related factors.
In the urban development literature, AI and the IoT are fundamental [69] for the construction of truly intelligent cities. According to the authors, it is possible to design citizen-centric smart city models by combining the aforementioned technologies, obtaining massive quantities of data from all smart city services and facilities, automating processes within a city to improve efficiency and promote sustainable urban development, fostering the economy, providing opportunities for citizens, and preserving the environment. Both the IoT and AI have the benefit of enabling the real-time analysis and addressal of issues, which are essential to improving all of the operations inside a smart city. Data collection used to be quite costly since the IoT and sensor technology were not yet standardized. However, contemporary advancements have reduced the cost of sensors, computation, and storage, thus enabling their widespread application throughout cities.
The most significant disruptive technologies with respect to the creation of the smart city are also examined by [68], and some new ones are currently being introduced. They believe that every smart city is a dynamic, intricate system that draws an increasing number of people in the quest for realizing urbanization’s advantages. By 2050, 68% of the world’s population will reside in cities, which poses issues due to the lack of infrastructure and a variety of resources such as energy, water, fuel, transportation systems, etc. New and emerging technologies are being developed to address these issues, including the IoT, big data, blockchain, AI, data analytics, and ML and cognitive learning, with the aim of bringing forth several changes in important spheres of urban development, such as health, energy, transportation, education, and public safety, among others. Disruptive technologies, according to the authors [68], are a major force behind the development of smart cities. They determined the most prevalent ones that, via integration, make cities smarter by providing citizens with improved living circumstances and simpler access to goods and services.
The need for AI-enabled innovations has risen with the emergence of smart cities, which are metropolitan areas that use community, technology, and policy to bring productivity, innovation, livability, wellness, sustainability, accessibility, good governance, and excellent planning [66]. The authors of [74] have elaborated on the importance of water management, quality, and availability by indicating the importance of deep learning as a disruptive force in urban water management. These authors carried out a review on the existing literature and revealed some of the practical effects of this disruptive technology on water management, such as anomaly detection, system state forecasting, asset monitoring, and assessment. They conclude that urban water systems should be enhanced by deep learning to become highly intelligent and autonomous.
To achieve a technological revolution, disruptive digital technologies [75] must be used in the built environment and its related sectors, including construction, city planning, real estate, architecture, and urban planning. Therefore, integrated smart city, construction, and real estate goals may be accomplished in line with the United Nation’s Sustainable Development Goals to foster sustainable development in the smart city environment [76]. Such Industry 4.0-compliant disruptive technologies (a.k.a. Smart Tech 4.0) have been established in a number of smart city settings [77]. More than 20 such technologies have thus far been realized, including AI, big data, IoT, Unmanned Aerial Vehicles (UAVs), clouds, 3D scanning and printing, wearable technologies, wireless technologies, VR, AR, Mixed Reality (XR), robotics, blockchains, Software as a Service (SaaS), digital twins, ML, ubiquitous computing, mobile computing, renewable energy, autonomous vehicles, and 5G communications [76]. The authors believe that despite the built environment’s enormous potential for the adoption and uses of these technologies, there has only been little progress made with respect to their implementation.
The authors of [68] bring into attention the use of a Convolutional Neural Network (CNN) as a disruptive technology applied in smart cities. By using the new information in a user’s request to enhance the neural network by a new epoch, an AI agent (AIA) surpasses the trained framework, as it gains knowledge while working. The authors suggest that humankind may successfully construct a far more prosperous and powerful smart city economy by using the appropriate AIA or CNN with the data supplied from a blockchain.
Anywhere there are large volumes of data and users who are learning from these data there is a comparable potential. Thus, the AIA and/or CNN can be used to control traffic and other issues involving city transportation, education, and health.

6.3. Smart Government

Standardized frameworks and procedures for integrating technology, citizens, and governments are necessary to allow cities to become smart. The potential use of blockchain technology as a facilitator for e-governance in smart cities has been explored by numerous academics [78] by assessing the issues that citizens confront on a daily basis and contrasting them with the benefits offered by blockchain integration. The usage of blockchain technology is now widespread and is increasing daily. As a result, all smart cities now have smart governments thanks to their blockchains.
A contradictory use [79] of disruptive technologies emerged in Australia, where the government is of the opinion that there is a great opportunity to use market capabilities to offer evaluations against visa requirements. Utilizing business service solutions, well-established enabling technology (such as RPA and data analytics), and cutting-edge disruptive technologies (such as AI ML), the automation of visa processing can be achieved. The federal government in Australia is drawing on disruptive technologies to fast-track visa processes and transform the current system while negating 3000 jobs.
A new disruptive concept was observed by [80] regarding the implementation of IT in the public sector. The authors debate the integration of AI and IoT and the emergence of the AI of Things (AIoT) [36]. The study intends to identify the drivers of and constraints on AIoT integration in the public sector and provide a modular framework for it. According to the authors, this effort is crucial for developing laws and policies that would maximize the potential advantages for public institutions in the governmental sector.


  1. Bower, L.J.; Christensen, M.C. Disruptive technologies: Catching the wave. Har. Buss. Rev. 1995, 73, 43–53.
  2. Christensen, C.M.; Bower, J.L. Customer power, strategic investment, and the failure of leading firms. Strateg. Manag. J. 1996, 17, 197–218.
  3. Christensen, C.M. The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail; Harvard Business School Press: Boston, MA, USA, 1997; 179p.
  4. O’Connor, S.; Sidorko, P. Chapter 2—The complexities of our informational environment. In Imagine Your Library’s Future; Chandos Publishing: Oxford, UK, 2010; pp. 33–60.
  5. Laukyte, M. Disruptive Technologies and the Sport Ecosystem: A Few Ethical Questions. Philosophies 2020, 5, 24.
  6. Jekov, B.; Petkova, P.; Parusheva, Y.; Shoikova, E. Disruptive Technologies—Artificial Intelligence and Blockchain in Education. In Proceedings of the 11th Annual International Conference of Education, Research and Innovation (ICERI), Seville, Spain, 7–9 November 2022; pp. 6784–6793.
  7. Bongomin, O.; Ocen, G.G.; Nganyi, E.O.; Musinguzi, A.; Omara, T. Exponential Disruptive Technologies and the Required Skills of Industry 4.0. J. Eng. 2020, 2020, 4280156.
  8. Hernández, R. World Standards Day 2018 Puts the Spotlight on the Fourth Industrial Revolution. Available online: (accessed on 19 January 2023).
  9. Bird, K. Four Trends Will Impact ISO’s Future Strategy. Available online: (accessed on 19 January 2023).
  10. Manickam, P.; Mariappan, S.A.; Murugesan, S.M.; Hansda, S.; Kaushik, A.; Shinde, R.; Thipperudraswamy, S.P. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors 2022, 12, 562.
  11. Kelly, J.T.; Collins, P.F.; McCamley, J.; Ball, L.; Roberts, S.; Campbell, K.L. Digital disruption of dietetics: Are we ready? J. Hum. Nutr. Diet. 2021, 34, 134–146.
  12. Joda, T.; Yeung, A.W.K.; Hung, K.; Zitzmann, N.U.; Bornstein, M.M. Disruptive Innovation in Dentistry: What It Is and What Could Be Next. J. Dent. Res. 2021, 100, 448–453.
  13. Ahmad, P.; Alam, M.; Aldajani, A.; Alahmari, A.; Alanazi, A.; Stoddart, M.; Sghaireen, M. Dental Robotics: A Disruptive Technology. Sensors 2021, 21, 3308.
  14. McBee, M.P.; Wilcox, C. Blockchain Technology: Principles and Applications in Medical Imaging. J. Digit. Imaging 2020, 33, 726–734.
  15. Rasouli, J.J.; Shao, J.N.; Neifert, S.; Gibbs, W.N.; Habboub, G.; Steinmetz, M.P.; Benzel, E.; Mroz, T.E. Artificial Intelligence and Robotics in Spine Surgery. Glob. Spine J. 2021, 11, 556–564.
  16. Dorweiler, B.; Wegner, M.; Salem, O.; Murtaja, A.; Schäfers, J.F.; Oberhuber, A. Innovation, disruptive technologies and transformation in vascular surgery. Gefasschirurgie 2022, 27, 561–568.
  17. Mohanty, K.; Subiksha, S.; Kirthika, S.; Bh, S.; Sokkanarayanan, S.; Bose, P.; Sathiyanarayanan, M.; IEEE. Opportunities of Adopting AI-Powered Robotics to Tackle COVID-19. In Proceedings of the International Conference on COMmunication Systems and NETworkS (COMSNETS), Bangalore, India, 5–9 January 2022; pp. 703–708.
  18. Jabarulla, M.Y.; Lee, H.N. A Blockchain and Artificial Intelligence-Based, Patient-Centric Healthcare System for Combating the COVID-19 Pandemic: Opportunities and Applications. Healthcare 2021, 9, 1019.
  19. Saraswat, D.; Bhattacharya, P.; Verma, A.; Prasad, V.K.; Tanwar, S.; Sharma, G.; Bokoro, P.N.; Sharma, R. Explainable AI for Healthcare 5.0: Opportunities and Challenges. IEEE Access 2022, 10, 84486–84517.
  20. Mesko, B.; Hetenyi, G.; Gyorffy, Z. Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv. Res. 2018, 18, 545.
  21. Maliha, G.; Gerke, S.; Cohen, G.; Parikh, R.B. Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation. Milbank Q. 2021, 99, 629–647.
  22. Khatab, Z.; Yousef, G.M. Disruptive innovations in the clinical laboratory: Catching the wave of precision diagnostics. Crit. Rev. Clin. Lab. Sci. 2021, 58, 546–562.
  23. Brunelle, F.; Brunelle, P. Artificial Intelligence and Medical Imaging: Definition, State of the Art and Perspectives. Bull. Acad. Natl. Med. 2019, 203, 683–687.
  24. Garbuio, M.; Lin, N. Artificial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models. Calif. Manag. Rev. 2019, 61, 59–83.
  25. Prakash, S.; Balaji, J.N.; Joshi, A.; Surapaneni, K.M. Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare—A Scoping Review of Reviews. J. Pers. Med. 2022, 12, 1914.
  26. Mesko, B. Future Directions of Digital Health. In Digital Health: Scaling Healthcare to the World; Rivas, H., Wac, K., Eds.; Springer: Berlin/Heidelberg, Germany, 2018; pp. 339–363.
  27. Ojo, A. Next Generation Government—Hyperconnected, Smart and Augmented. In Proceedings of the 20th IFIP WG 5.5 Working Conference on Virtual Enterprises (PRO-VE), Turin, Italy, 23–25 September 2019; pp. 285–294.
  28. Ljepava, N. AI-Enabled Marketing Solutions in Marketing Decision Making: AI Application in Different Stages of Marketing Process. TEM J. 2022, 11, 1308–1315.
  29. Wiegandt, D. Blockchain and Smart Contracts and the Role of Arbitration. J. Int. Arbitr. 2022, 39, 671–690.
  30. Noor, A. Adoption of Blockchain Technology Facilitates a Competitive Edge for Logistic Service Providers. Sustainability 2022, 14, 15543.
  31. Ugochukwu, N.A.; Goyal, S.B.; Rajawat, A.S.; Islam, S.M.N.; He, J.; Aslam, M. An Innovative Blockchain-Based Secured Logistics Management Architecture: Utilizing an RSA Asymmetric Encryption Method. Mathematics 2022, 10, 4670.
  32. Zondervan, N.A.; Tolentino-Zondervan, F.; Moeke, D. Logistics Trends and Innovations in Response to COVID-19 Pandemic: An Analysis Using Text Mining. Processes 2022, 10, 2667.
  33. Hong, Z.F.; Zhang, H.Y.; Gong, Y.M.; Yu, Y.G. Towards a multi-party interaction framework: State-of-the-art review in sustainable operations management. Int. J. Prod. Res. 2022, 60, 2625–2661.
  34. Yu, H. Modeling a remanufacturing reverse logistics planning problem: Some insights into disruptive technology adoption. Int. J. Adv. Manuf. Technol. 2022, 123, 4231–4249.
  35. Jia, W.; Wang, S.; Xie, Y.; Chen, Z.; Gong, K. Disruptive technology identification of intelligent logistics robots in AIoT industry: Based on attributes and functions analysis. Syst. Res. Behav. Sci. 2022, 39, 557–568.
  36. Cukier, W. Disruptive processes and skills mismatches in the new economy Theorizing social inclusion and innovation as solutions. J. Glob. Responsib. 2019, 10, 211–225.
  37. Oosthuizen, R.M. The Fourth Industrial Revolution—Smart Technology, Artificial Intelligence, Robotics and Algorithms: Industrial Psychologists in Future Workplaces. Front. Artif. Intell. 2022, 5, 913168.
  38. Khatri, S.; Pandey, D.K.; Penkar, D.; Ramani, J. Impact of Artificial Intelligence on Human Resources. In Data Management, Analytics and Innovation; Advances in Intelligent Systems and Computing; Sharma, N., Chakrabarti, A., Balas, V., Eds.; Springer: Singapore, 2019.
  39. Burden, K. Impact of disruptive technologies on sourcing and outsourcing transactions. Comput. Law Secur. Rev. 2018, 34, 886–889.
  40. Miklosik, A.; Evans, N. Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review. IEEE Access 2020, 8, 101284–101292.
  41. Koizumi, S. The Light and Shadow of the Fourth Industrial Revolution. In Innovation beyond Technology: Science for Society and Interdisciplinary Approaches; Lechevalier, S., Ed.; Springer: Berlin/Heidelberg, Germany, 2019; pp. 63–86.
  42. Rosales, M.A.; Magsumbol, J.A.V.; Palconit, M.G.B.; Culaba, A.B.; Dadios, E.P. Artificial Intelligence: The Technology Adoption and Impact in the Philippines. In Proceedings of the IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines, 3–7 December 2020.
  43. Anitha, J.; Saranya, N. Cassava Leaf Disease Identification and Detection Using Deep Learning Approach. Int. J. Comput. Commun. Control 2022, 17, 1–7.
  44. Ortola, A. Unlocking the full potential of artificial intelligence in PH. Philippine Daily Inquirer, 24 June 2019.
  45. Karimanzira, D.; Rauschenbach, T. An intelligent management system for aquaponics. at-Automatisierungstechnik 2021, 69, 345–350.
  46. Jacobs, M.; Remus, A.; Gaillard, C.; Menendez, H.M.; Tedeschi, L.O.; Neethirajan, S.; Ellis, J.L. ASAS-NANP symposium: Mathematical modeling in animal nutrition: Limitations and potential next steps for modeling and modelers in the animal sciences. J. Anim. Sci. 2022, 100, skac132.
  47. De Bernardi, P.; Azucar, D.; Forliano, C.; Franco, M. Innovative and Sustainable Food Business Models. In Innovation in Food Ecosystems: Entrepreneurship for a Sustainable Future; Springer: Berlin/Heidelberg, Germany, 2020; pp. 189–221.
  48. Neethirajan, S.; Kemp, B. Digital Twins in Livestock Farming. Animals 2021, 11, 1008.
  49. Spanaki, K.; Sivarajah, U.; Fakhimi, M.; Despoudi, S.; Irani, Z. Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Ann. Oper. Res. 2022, 308, 491–524.
  50. Kamble, S.; Gunasekaran, A.; Sharma, R. Modeling the blockchain enabled traceability in agriculture supply chain. Int. J. Inf. Manag. 2020, 52, 101967.
  51. Wamba, S.F.; Queiroz, M.M. Blockchain in the operations and supply chain management: Benefits, challenges and future research opportunities. Int. J. Inf. Manag. 2020, 52, 102064.
  52. Moore, W.B.; Felo, A. The evolution of accounting technology education: Analytics to STEM. J. Educ. Bus. 2022, 97, 105–111.
  53. Brito, C.R.; Ciampi, M.M.; Sluss, J.J.; Santos, H.D. Trends in Engineering Education: A Disruptive View for not so far Future. In Proceedings of the 18th International Conference on Information Technology Based Higher Education and Training (ITHET), Magdeburg, Germany, 26–27 September 2019.
  54. Ghani, A. Engineering education at the age of Industry 5.0—Higher education at the crossroads. World Trans. Eng. Technol. Educ. 2022, 20, 112–117.
  55. Bühler, M.M.; Jelinek, T.; Nübel, K. Training and Preparing Tomorrow’s Workforce for the Fourth Industrial Revolution. Educ. Sci. 2022, 12, 782.
  56. Chaka, C. Is Education 4.0 a Sufficient Innovative, and Disruptive Educational Trend to Promote Sustainable Open Education for Higher Education Institutions? A Review of Literature Trends. Front. Educ. 2022, 7, 226.
  57. Choi, T.-M.; Kumar, S.; Yue, X.; Chan, H.-L. Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond. Prod. Oper. Manag. 2022, 31, 9–31.
  58. Bengoechea, J.; Bell, A. The Impact of 21st Century Technology in Higher Education: The Role of Artificial Intelligence. Int. J. Educ. Pedagog. Sci. 2022, 16, 493–496.
  59. Miao, F.; Wayne, H.; Ronghuai, H.; Hui, Z. AI and Education: A Guidance for Policymakers; UNESCO Publishing: Paris, France, 2021.
  60. Pornpongtechavanich, P.; Eambunnapong, K.; Daengsi, T.; Nilsook, P. Critical success factors for smart-professional disruptor in university. Int. J. Eval. Res. Educ. 2022, 11, 1696–1703.
  61. Molenaar, I. Towards hybrid human-AI learning technologies. Eur. J. Educ. 2022, 57, 632–645.
  62. Holmes, W.; Tuomi, I. State of the art and practice in AI in education. Eur. J. Educ. 2022, 53, 542–570.
  63. Zeeshan, K.; Hämäläinen, T.; Neittaanmäki, P. Internet of Things for Sustainable Smart Education: An Overview. Sustainability 2022, 14, 4293.
  64. Ciolacu, M.; Svasta, P.M.; Berg, W.; Popp, H.; IEEE. Education 4.0 for Tall Thin Engineer in a Data Driven Society. In Proceedings of the 23rd IEEE International Symposium for Design and Technology in Electronic Packaging (SIITME), Constanta, Romania, 26–29 October 2022; pp. 438–443.
  65. Yang, K.; Shi, Y.M.; Zhou, Y.; Yang, Z.P.; Fu, L.Q.; Chen, W. Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface. IEEE Netw. 2020, 34, 16–22.
  66. Kasinathan, P.; Pugazhendhi, R.; Elavarasan, R.M.; Ramachandaramurthy, V.K.; Ramanathan, V.; Subramanian, S.; Kumar, S.; Nandhagopal, K.; Raghavan, R.R.V.; Rangasamy, S.; et al. Realization of Sustainable Development Goals with Disruptive Technologies by Integrating Industry 5.0, Society 5.0, Smart Cities and Villages. Sustainability 2022, 14, 15258.
  67. Sgantzos, K.; Grigg, I. Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications. Future Internet 2019, 11, 170.
  68. Corchado, J.M.; Trabelsi, S. Advances in Sustainable Smart Cities and Territories. Electronics 2022, 11, 1280.
  69. Xu, Y. Programmatic Dreams: Technographic Inquiry into Censorship of Chinese Chatbots. Soc. Media + Soc. 2018, 4, 2056305118808780.
  70. Zweispace. Zweispace Won the Best Startup Award for Real Estate and Construction Batch at the Plug and Play Summer Summit 2020. Available online: (accessed on 4 August 2022).
  71. Ortega-Fernández, A.; Martín-Rojas, R.; García-Morales, V. Artificial Intelligence in the Urban Environment: Smart Cities as Models for Developing Innovation and Sustainability. Sustainability 2020, 12, 7860.
  72. Schintler, L.A.; McNeely, C.L. Artificial intelligence, institutions, and resilience: Prospects and provocations for cities. J. Urban Manag. 2022, 11, 256–268.
  73. Fu, G.; Jin, Y.; Sun, S.; Yuan, Z.; Butler, D. The role of deep learning in urban water management: A critical review. Water Res. 2022, 223, 118973.
  74. Ullah, F. Smart Tech 4.0 in the Built Environment: Applications of Disruptive Digital Technologies in Smart Cities, Construction, and Real Estate. Buildings 2022, 12, 1516.
  75. Ullah, F.; Sepasgozar, S.M.E.; Wang, C.X. A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms. Sustainability 2018, 10, 3142.
  76. Ghobakhloo, M. Industry 4.0, digitization, and opportunities for sustainability. J. Clean. Prod. 2020, 252, 119869.
  77. Khanna, A.; Sah, A.; Bolshev, V.; Jasinski, M.; Vinogradov, A.; Leonowicz, Z.; Jasiński, M. Blockchain: Future of e-Governance in Smart Cities. Sustainability 2021, 13, 11840.
  78. McLean, J.; Mackenzie, R. Digital justice in Australian visa application processes? Altern. Law J. 2019, 44, 291–296.
  79. Ishengoma, F.R.; Shao, D.; Alexopoulos, C.; Saxena, S.; Nikiforova, A. Integration of artificial intelligence of things (AIoT) in the public sector: Drivers, barriers and future research agenda. Digit. Policy Regul. Gov. 2022, 24, 449–462.
  80. Rapanyane, M.B.; Sethole, F.R. The rise of artificial intelligence and robots in the 4th Industrial Revolution: Implications for future South African job creation. Contemp. Soc. Sci. 2020, 15, 489–501.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : ,
View Times: 534
Revisions: 3 times (View History)
Update Date: 07 Jul 2023