Industry 4.0 in healthcare involves use of a wide range of modern technologies including digitisation, artificial intelligence, user response data (ergonomics), human psychology, the Internet of Things, machine learning, big data mining, and augmented reality to name a few. The healthcare industry is undergoing a paradigm shift thanks to Industry 4.0, which provides better user comfort through proactive intervention in early detection and treatment of various diseases. The sector is now ready to make its next move towards Industry 5.0, but certain aspects that motivated this review paper need further consideration. As a fruitful outcome of this review, we surveyed modern trends in this arena of research and summarised the intricacies of new features to guide and prepare the sector for an Industry 5.0-ready healthcare system.
The Fourth Industrial Revolution, otherwise known as Industry 4.0, is advancing healthcare to unprecedented comfort levels on the foundation of digitisation, artificial intelligence, and 5G telecommunication [1,2]. In this context, Table 1 summarise various definitions used currently in the context of Industry 4.0 to explain many of its subsystems. These factors have helped in many ways to combat the ongoing crisis the world is facing in the wake of the COVID-19 pandemic [3,4,5,6].
Different digital projects have been developed globally by incorporating digital diagnostic systems which have significantly improved agility in X-ray and MRI investigations. This has, in turn, allowed quick diagnosis of patients’ healthcare data retrospectively as well as clinical anamnesis to provide prompt feedback [7,8]. A question worthy to be asked at this stage is: what is next? The answer to this question primarily drove this review. As shown in Figure 1, the review begins by providing an insight into the interoperable development of the current ecosystem involving people, industry, business, and the government, which forms the backbone of Industry 4.0 in sharp contrast to the previous industrial revolutions. In modern times, machines have become sufficiently intelligent to make decisions in real time and to feed those decisions through cloud-based technologies  using neural networks [10,11] and decision-support systems . Figure 2 shows the core components and essential elements of an Industry 4.0 system.
Table 1. Definitions of critical elements in an Industry 4.0 system.
Figure 1. Evolution of industrial developments over time.
Figure 2. Building blocks of an Industry 4.0 system.
In fact, the use of deep neural networks has enabled AI to make unprecedented improvements to quality of learning. For example, working with Alexa, Google Search, and Yandex Disc has helped learning over time and the more these tools are used, the more the system becomes trained.
There are numerous examples of use of Internet-of-Things (IoT)-enabled systems which can be seen in day-to-day life. An Amazon store without cash registers or cashiers with the capability to charge users simply based on their body movements is an excellent example, while another involving the use of IoT include Uber, Ola, and GetTaxi. Recently, Lv et al.  investigated the issue of quality service and network loading for next generation IoT systems. Additionally, environmental aspects of Industry 4.0 are now also being explored [2,60].
Table 2 highlights state-of-the-art use of advanced technologies in healthcare and medicine revealed by different research papers. This review paper is the first to highlight the prospects of the Industrial Internet of Things (IIoT) in the healthcare sector. Table 2 highlights the novelty of this review paper vis-a-vis increasing interest of the scientific community in this area.
Table 2. The key technologies discussed in the recent review publications (adapted and expanded from ). Here, “V” stands for the presence, and “X” for the absence of discussion on the relevant topics that make up an Industrial 4.0 system.
Recently, Austin et al.  investigated collaboration between academia, SMEs and digital health industries for the promotion of innovative digital solutions in healthcare. Qadri et al.  presented an extensive review of IoT applications in healthcare with careful articulation of the previous literature in this field. They introduced the term H-IoT (Healthcare IoT) to emphasize the importance of IoT in the field of healthcare and medicine. Marques et al.  presented a review on IoT applications in healthcare highlighting the need of medical professionals, students, and engineers. They discussed the advantages of IoT platforms in achieving personalized healthcare and developing smart devices for diagnosis and monitoring. They also pointed out the limitations on social readiness . Hau et al.  showed how the digital tools of Industry 4.0 could be used to combat COVID-19 pandemic. Von Eiff et al. , in their short review, discussed prospects of digitalisation in healthcare. Their work partly discussed digital development and the use of Industry 4.0 tools in medicine development.
From this brief discussion, the importance of Industry 4.0 in the healthcare sector is obvious. Thus, this review highlights state-of-the-art digitalisation of medicine and healthcare and alludes to the sharp transition this sector is facing while moving towards Industry 5.0. This review also aims to discuss the trends in digital medicine and healthcare and to provide future directions in this area.
2. Ingredients of an Industry 4.0 Healthcare System
2.1. Internet of Things (IoT)
IoT is a term that refers to any device with network access [13,26,70,71]. Modern devices/objects/networks of objects/systems are equipped with sensors, software, and network equipment. The network equipment and these sensors are capable of compiling and processing data arrays using internet [2,70,72,73,74,75] protocols.
5G has made an enormous impact on IoT technology and economy due to its superior level of connectivity and improved functionality. The key 5G technology drivers are superfast broadband, ultra-reliable low latency communication, massive machine-type communications, high reliability/availability, and efficient energy usage [73,76,77,78].
The main area of applications of 5G-enabled IoT are the tracking of goods and materials, asset monitoring, remote data collection, self-service systems, remote service delivery systems, real-time market data, and flexible pricing models [71,79]. As per the review of Likens et al. , it would appear as shown in Figure 3 that the Internet of Things will lead to be the most promising techniques that will change the gamut for industries and academia in the post-Fourth Industrial Revolution era.
Figure 3. Leading position of IoT in Industry 4.0 structure .
Most modern industries utilise modelling and simulations for process monitoring, control, diagnosis, optimisation, and design. Industry 4.0 and massive digitisation have made it possible to collect and process large arrays of data, resulting in the development of data-driven decisions and modelling tools . It is worth mentioning that data-driven, statistical, or empirical models do not require broad initial knowledge about the studied system, but strongly rely on the presence of data collected from the process . Modern simulation tools are used for predicting natural disasters which might lead to many victims (e.g., tsunami) [83,84,85,86]. A new trigger for modelling advancement re-emerged in recent years due to the development of machine learning techniques and a variety of Industry 4.0 technologies. Big data and modern modelling and analytical tools provide new horizons even to address old legacy issues and open new scenarios for realising innovative ideas.
2.2. Artificial Intelligence
Artificial intelligence (AI) allows computers to learn from their own experience, adapt to given parameters, and perform tasks that were previously only possible for humans. In most AI implementations such as computer chess players or self-driving cars, the role of deep learning and natural language processing is critical. AI allows automation of repetitive learning and searching processes using data acquisition to identify trends. Forms of AI in use today include digital assistants, chatbots, deep learning, and machine learning [16,17,18,19,20,21,22,23,87,88,89].
2.3. Big Data Analytics (BDA)
Big data analytics (BDA) is one of the key components of Industry 4.0. Big data technology deals with large arrays of data, enabling the derivation of information relevant for rapid decision-making. The derived data is transformed into the relevant goal-oriented knowledge to help achieve agility in problem solving [47,48]. The successful application of BDA in online trade can be seen through AliExpress, Amazon, and eBay. Technologies for image data are also rapidly developing enabling target recognition, photo filtering, and stereoscopic three-dimensional (3D) contents [90,91,92,93].
2.4. Digital Manufacturing and Advanced Materials Processing
One of the main outcomes of the advances in digital manufacturing is 3D printing technology, also called additive manufacturing (AM) [29,30,31,32,33]. AM enables the processing of polymers, ceramics, glass, and metallic alloys. Using approaches such as the Design for Additive Manufacturing (DfAM)  and Materials Design by Additive Manufacturing (MaDe-by-AM) , novel materials can now be manufactured with ease, which includes tailored composition as well as structural and functionally graded materials [32,35,36,37,38,39,96,97,98]. By shape and composition complexity, the design of new porous materials and metamaterials can also be fabricated. Moreover, the flexibility of maneuvering the printing head allows on-site printing of freeform shapes, which are potentially useful to develop custom-sized implants or prostheses [31,99,100]. Digitalisation of industrial manufacturing is developing due to the implementation of design strategies for new materials development [95,96,101,102]. Now, these additive technologies allow the printing of concrete buildings/structures . Additive manufacturing of concrete structures is much more promising for fast construction in complex natural environments compared to other techniques [104,105,106].
2.5. Green Aspects of Industry 4.0
Among other aspects, the environmental aspects of Industry 4.0 deserve a special mention. Some of those aspects in relation to food-water-energy nexus are highlighted below:
1. The survival of humanity will largely depend on how we address the following concerns in the upcoming years:
• Global energy shortage and depletion of raw materials (energy crisis) [107,108,109];
• Reduction of arable land, decrease in soil fertility, and food shortage (food crisis) ;
• Depleting availability of clean water 
• Catastrophic state of the environment (ecological crisis) [60,112,113,114].
2. Main spheres of life such as industry, transport, the fuel and energy complex, the economy, public administration, and security have taken new forms. This is due to the penetration of digital technologies into everyday life and the development of alternative energy and electrical vehicles .
3. Modern industrial development cannot proceed without efficient re-use and recycling procedures [116,117,118,119].
3. Digitalisation in Medicine
The term Medicine 4.0 is closely related to Industry 4.0; it describes the fourth stage in the development of medicine. Modern medicines which emerged around 150 years ago are undergoing a digital journey with the help of robotics, internet and artificial intelligence. The introduction of AI systems in medicine is one of the most important modern trends in world healthcare. Modern medical treatments cannot achieve their full potential without using advanced computing technologies. AI technologies are fundamentally changing the global healthcare system, allowing a radical redesign of the system of medical diagnostics, the development of new drugs, advanced analysis, testing, and treatment to enable advances in the field of transplantation surgeries [50,51,120,121]. Computational simulation using finite element analysis (FEA) is a crucial part of the digitalisation process in medicine [122,123]. FEA allows medical engineers/industrial designers to study many inter-related concepts including, for instance, device stability and durability (e.g., predicting end-of-life of patient-specific implants). FEA enables modelling of stresses within a material under different thermodynamic conditions . In an FEA model, the part is simulated and analyzed using representative physical behavior [122,125,126,127]. Such an approach demonstrates weak areas of the part, and it allows enhancement of the design. Digitalisation and AI generally improve the quality of healthcare services while reducing costs for medical clinics. Figure 4 highlights key technologies enabling digitalisation of medicine.