Industry 4.0 technologies and healthcare: a review of the current landscape and future prospects Open Access

Addressing healthcare challenges has become a major social and economic concern worldwide (Rathi et al., 2023; Núñez-Merino et al., 2025). The growing demand for effective healthcare and improved quality of life are putting great pressure on healthcare systems (Kaswan et al., 2019). A comprehensive revaluation of healthcare infrastructures worldwide has been driven by the COVID-19 pandemic, calling for innovative solutions to address the challenges (Javaid et al., 2020). The adoption of Industry 4.0 (I4.0) has become critical owing to the demand for cutting-edge solutions (Javaid and Haleem, 2019).

I4.0 encapsulates advanced manufacturing and information technologies to meet diverse human needs efficiently in reduced timeframes (Bonamigo et al., 2024a; Kaswan et al., 2025). These advancements elevate automated processes by promoting wireless communication in the manufacturing and service sectors (Bonamigo et al., 2024b). Since the early 1990s, Information and Communication Technologies (ICTs) have enhanced healthcare access, efficiency, and quality (Aceto et al., 2018). Cyber-Physical Systems (CPS), considered the backbone of Industry 4.0, integrate computing, communication, and control while relying on Big Data Analytics (BDA), Cloud/Fog computing, and IoT (Shrouf et al., 2014).

The healthcare sector is shifting to patient-centric care and timely meetings of their basic needs (Nice, 2016). Rapid design and development of medical components have been made possible through advanced software coupled with digital manufacturing technologies such as 3D printing (Seoane-Viaño et al., 2021). The widespread adoption of advanced ICTs is not only changing the face of the healthcare sector but also bringing new opportunities and enhancing prior applications. It has enabled teams to work effectively, share patient knowledge, and deliver coordinated care. First-hand health monitoring and medical care have become more accessible, contributing to improved treatment methods and more efficient healthcare management (Aceto et al., 2018). The implementation of these technologies brings ICT-related concerns into the healthcare industry, aggravating problems with system design, performance, data privacy, and security (Darshan and Anandakumar, 2015).

While studies on I4.0 in healthcare highlight various technological advancements, there is still limited research consolidating their practical applications and their outcomes across different healthcare domains. The existing literature lacks an exploration of how these technologies influence the daily workflows of healthcare professionals, their decision-making processes, and overall operational efficiency in clinical settings (Chaudhary et al., 2024; Kaswan et al., 2024). Another critical concern is patient privacy and data security as healthcare systems become increasingly digitized. However, research providing a thorough evaluation of how well I4.0 technologies address these concerns is still developing. Although the literature has studies related to applications of I4.0 in healthcare, how these technologies can be applied in different aspects of healthcare to improve operational performance and service quality is not explored (Inuwa et al., 2022; Hussain et al., 2023). No studies have pinpointed areas that can be addressed through I4.0 to make the healthcare system more conducive to patients. Furthermore, the literature lacks a comprehensive method or framework from the lens of I4.0 technologies that can revolutionize healthcare is also not reported.

Addressing these gaps will provide a better understanding of I4.0’s role in transforming healthcare which may guide future policy and practice. This makes it important to examine how I4.0 technologies are being adopted and applied in the healthcare industry. The following research questions have been framed to guide the findings of the present study.

To analyse the role of I4.0 technologies in the healthcare sector and the barriers to their successful implementation, the study reviews the academic literature on I4.0 applications in healthcare. A systematic approach to review the literature has been adopted while conducting a bibliometric analysis of relevant publications sourced from the Scopus database. By identifying prominent research themes, the study aims to uncover existing gaps in the literature and provide future research directions. While existing literature has focused on specific I4.0 technologies or isolated aspects, our study adopts a holistic approach. We offer a structured approach or framework to I4.0 deployment in healthcare, integrating technological readiness, impact evaluation, adoption enablers, and security compliance. Our study guides stakeholders toward a balanced approach that not only considers the potential benefits but also the potential risks of implementing I4.0 technologies in the healthcare sector. Policymakers can design regulations that encourage responsible I4.0 adoption while safeguarding patient rights.

The remainder of the document has been structured as follows: Section 2 covers the review of literature on I4.0 and its applications in healthcare while highlighting the gaps in literature; Section 3 outlines the research methodology, including how the final corpus of reviewed articles was obtained and the different bibliometric analysis techniques that were used; Section 4 presents the study’s findings and includes discussions around them; Section 5 discusses the study’s implications; and Section 6 provides the conclusion of the study along with highlighting its limitations and suggesting future research avenues.

The literature review section has been subdivided into three sub-sections. Section 2.1 highlights the background of Industry 4.0, Section 2.2 depicts Industry 4.0 technologies within healthcare and Section 3 illustrates the gaps identified from the literature.

Introduced in Germany in 2011, I4.0 became popular worldwide for enabling smart automation and data-driven decision-making, enhancing efficiency and productivity in manufacturing (Diniz et al., 2025). It encompasses six principles, including interoperability, virtualization, decentralization, real-time data capability, service orientation, and modularity.

It brings together a range of interconnected technologies by integrating big data, IoT, and CPS to enhance efficiency and adaptability in modern industry. These innovations enable self-optimizing systems capable of real-time monitoring and automated decision-making (Gupta et al., 2024; Quiroz-Flores et al., 2024). The workforce is evolving as automation creates opportunities in AI, robotics, and data science, with a projected increase in robots, smart sensors, and connected devices contributing to greater industrial flexibility and efficiency (Ortt et al., 2020). I4.0 is transforming economies by increasing productivity and enhancing supply chains through predictive analytics while reducing energy consumption and waste (Chiu and Fong, 2023). Together, smart factories and interconnected systems are driving an era of automation and digital transformation across industries (Costa et al., 2024). Various sectors, including healthcare, have seen substantial change because of the emergence of I4.0. These technologies have been adopted in the data-intensive and resource-constrained healthcare industry to enhance patient engagement, clinical outcomes, and operational efficiency.

I4.0 has revolutionized healthcare by improving patient monitoring, diagnostics and treatment. Internet of Medical Things (IoMT), smart wearables, and Wireless Body Area Networks (WBANs) have enabled real-time health monitoring, early anomaly detection, and reduced hospital visits (Aceto et al., 2020; Perera et al., 2017). Smart hospitals have showcased improved workflow efficiency and patient safety through automated data collection and interconnected monitoring systems (Kotzias et al., 2022). AI has enhanced disease detection by achieving high accuracy in medical imaging while also improving treatment outcomes with personalized interventions (Rasool et al., 2024).

Healthcare data management has improved significantly through the adoption of cloud-based platforms, ensuring real-time access to patient records and better decision-making (Hussain et al., 2023), while fog computing minimizes latency, making it appropriate for emergency care and telemedicine (Shi et al., 2016). Robot-assisted surgeries have enhanced precision and safety by reducing human error and recovery times, while smart hospital infrastructure improves operational efficiency (Pal et al., 2021). The pharmaceutical sector has adopted I4.0 to automate processes and ensure regulatory compliance (McDermott et al., 2024). Data security is a major concern which can be dealt with by utilising blockchain technology to ensure decentralized health record management, preventing unauthorized access, and maintaining patient privacy (Chukwu and Garg, 2020; Shahnaz et al., 2019). Additionally, blockchain streamlines pharmaceutical supply chains, ensuring medication authenticity and preventing counterfeits (Kumar et al., 2024).

While there is a huge potential for I4.0 in revolutionizing healthcare, challenges to its adoption and implementation remain. The growing demand for secure and technology-enabled healthcare solutions emphasizes the importance of this study in advancing knowledge on the transformative role of Industry 4.0 in modern healthcare systems and providing solutions for its successful implementation.

While the application of I4.0 in healthcare has gained growing interest from academia and industry, several gaps remain unaddressed. Existing studies provide fragmented insights, failing to analyse the interdependencies among different I4.0 technologies comprehensively (Paul et al., 2021; Karatas et al., 2022). They have majorly focused on IoT, big data, AI, blockchain and cloud computing, and other emerging technologies, such as Augmented Reality (AR) and 3D printing, have not been sufficiently explored. There is also a need to examine the ethical considerations of adopting these technologies for patient-centric care. These gaps justify the conduct of the present systematic review study which aims to provide a comprehensive understanding of adopting and implementing I4.0 in healthcare while addressing concerns regarding privacy and safety for the successful deployment of such advanced technologies in healthcare.

The basis for utilizing a bibliometric analysis is its ability to measure the productivity and impact of publications while analysing the interrelationships between them. It incorporates statistical and mathematical methods to study the bibliographic data, such as the number of publications and citation counts, in order to recognize eminent writers, works, journals, organizations, and places associated with a specific field of study while also analysing the intellectual relationships between them (Kumar et al., 2023b). Science Mapping techniques, including bibliographic coupling and co-occurrence analysis, have been utilized to identify major research areas. Bibliographic coupling is useful for identifying interlinkages between publications based on common bibliographic information. It relies on citation data, which may not fully capture emerging trends, recent studies with low citations, or interdisciplinary influences (Kessler, 1963). This limitation can be addressed by conducting the co-occurrence analysis of keywords, which highlights the most frequently used keywords to help extend the analysis by identifying potential areas for future research (Chen et al., 2016).

The literature on I4.0 in healthcare was extracted using the Scopus database (Kumar et al., 2023b). To obtain pertinent papers connected to the study, certain keywords found in prior review studies were searched for in the Title, Abstract, and Keywords (Rathi et al., 2023), as shown in Table 1.

The search outcome was filtered to include only English Articles and Review Studies published in a Journal to maintain the quality of the present study. This resulted in a corpus of 712 articles published till 2024, which the authors further screened to retain only those publications that specifically talk of I4.0’s role in healthcare. The final corpus, as mentioned in Figure 1, includes 274 publications that have been taken up to conduct the bibliometric analysis using the VOSviewer software.

This section reveals notable contributors to the field of research of Industry 4.0 and its applications in the healthcare industry and notable contributors to the field. It emphasizes eminent authors, organizations, publications, sources, and countries that are disseminating significant research in the field at hand.

Figure 2 represents the year-wise publications related to I4.0 in healthcare. The research has grown steadily over the years, as reflected in the chart. Starting with a few publications in 2017, the field started growing consistently as I4.0 brought innovations in healthcare. The number of academic publications has risen significantly between 2021 and 2023, which can be attributed to the growing demand for innovative healthcare solutions during the COVID-19 pandemic. While there is a slight fall in publications in 2024, the overall upward trend highlights the increasing role of I4.0 in transforming healthcare.

The publications in the field have evolved from the conceptualization stage (early 2010s) to the implementation stage (2015–2018), with IoT-driven healthcare systems incorporating big data applications. The COVID-19 period (2020–2022) ignited the interest of academia in AI-driven diagnostics, remote monitoring, and telemedicine, while recent studies (2023–present) have emphasized 3D printing in pharmaceuticals, federated learning, and ethical concerns over the implementation of these advanced technologies.

Table 2 highlights the leading authors having more than three publications in the domain of I4.0 in healthcare. Abdul W. Basit has made a significant contribution with 12 publications in this area. Abdul W. Basit and Alvaro Goyanes have focused on pharmaceutical 3D printing and customized drug delivery. At the same time, Simon Gaisford and Moe Elbadawi made contributions towards AI-driven drug formulation and smart delivery systems. Luz Tortorella and Jose Arturo Garza-Reyes have studied digital healthcare transformation and supply chain resilience. Other researchers, including Jun Jie Ong, Flavio S. Fogliatto and Jiju Antony, have worked on improving healthcare efficiency and effectiveness through AI and medical CPS. In contrast, Neeraj Kumar has made advancements in IoT and fog computing for secure data management. Identifying top authors may help researchers align their research work with current trends and discover potential collaboration opportunities.

Table 3 highlights the top contributing countries in the field of research on I4.0 in healthcare based on the number of publications and citation count. Identifying top contributing countries reveals the global spread of research and innovation. This may help researchers and policymakers identify prominent research centres, leverage cross-border collaborations, and accelerate the adoption of advanced healthcare technologies worldwide. India emerged as the top contributor to the field with 73 publications, followed by the UK and the USA, whereas Australia stands out with a high citation rate (185.78 citations per paper), indicating fewer but highly influential studies. Developed nations like the UK, US, and Italy lead in both research volume and citation impact while emerging economies like India and China are producing a substantial amount of academic literature and are not far behind developed nations in terms of publication quality.

The focus of research may vary because of the regional differences in terms of economic development and healthcare challenges. Developed countries like USA and Germany focus on AI-driven diagnostics and automation, while emerging economies like India and Brazil emphasize cost-effective telemedicine and digital health solutions. Ethical considerations and regulatory frameworks are being emphasized more by European countries, whereas research in nations like South Korea and Singapore has focused on smart hospitals and 5G-enabled healthcare innovations.

Table 4 shows the leading sources publishing on I4.0 in healthcare. It includes the sources that have published more than two articles in the present field of research. IEEE Access stood ahead with 10 publications in the field and the highest citation count per publication. IEEE Transactions on Industrial Informatics and the Journal of Industrial Information Integration follow the lead in terms of citation rate (120.43 and 169.2 citations per paper, respectively). Identifying the influential publication sources can guide the researchers to target the journals that can increase the visibility and relevance of their research. Understanding the specific focus of the journals can help researchers strategically position their work to reach the right audience and increase citation count.

Table 5 represents the top-cited studies on I4.0 in healthcare. The high-impact scholarly work covers AI-driven diagnostics, IoT-enabled smart healthcare, real-time data processing, and digital transformation in pharmaceuticals while highlighting key challenges like data security and encryption.

The co-occurrence analysis revealed the major areas of research based on the keywords used prominently by the studies. The present study took into consideration keywords that appear in the reviewed articles at least three times. Figure 3 and Table 6 show six keyword clusters that can be categorized into the following groups: (1) Cybersecurity and Smart Systems in Healthcare; (2) Digital Health and Innovation; (3) Big Data and Supply Chain Management in Healthcare; (4) AI and ML in Healthcare and Pharma; (5) Sustainability in Healthcare and Pharma; and (6) Automation and Advanced Manufacturing in Pharma.

The advancements in healthcare have been made possible by the convergence of I4.0 technologies, such as AI-driven predictive maintenance for smart hospitals, blockchain for securing real-time DT, and federated learning for accelerated drug discovery while preserving data privacy. Integrating IoT-enabled smart healthcare with supply chain analytics can enhance medical resource distribution, whereas combining circular economy principles with big data can revolutionize sustainable healthcare waste management. These interlinkages will drive a more intelligent, secure, and sustainable healthcare ecosystem while ensuring efficiency and accessibility.

The section showcases the network map by undertaking bibliographic coupling of documents, as represented by Figure 4. The network has been divided into different interconnected clusters, based on which the field of research can be mapped, highlighting prominent sub-fields of research.

Six clusters have been generated that represent the major areas in which the research is being conducted, focusing on I4.0 in the healthcare sector. Some articles that fall under a specific cluster can be associated with other clusters as there exist some interrelationships among the clusters. The themes that are identified are discussed below.

This cluster examines the integration of Industry 4.0 technologies, demonstrating their role in improving operational efficiency, patient management, and supply chain optimization.

Integrating I4.0 technologies has been shown to improve healthcare systems by enabling personalized care and efficient data management (Karatas et al., 2022; Khanra et al., 2020; Kraus et al., 2021; Kumar et al., 2023a). These technologies can optimize hospital operations and optimize healthcare supply chains (Ilangakoon et al., 2022; Mustapha et al., 2021). During COVID-19, the quick production of PPE and contact tracing were made possible only with the help of these advancements (Javaid et al., 2020; Javaid and Haleem, 2020). However, the adoption of these technologies remains clouded with challenges such as regulatory barriers and workforce skill gaps, which must be addressed to unlock their full potential (Ajmera and Jain, 2019; Cavallone and Palumbo, 2020).

This cluster highlights how I4.0 technologies address challenges related to data management by leveraging BDA, blockchain, and cloud computing.

The reliance on digital infrastructure in healthcare is constantly increasing, making data management a critical concern of I4.0 applications. These interconnected technologies generate huge amounts of data, which makes it necessary to have robust security measures in place. Technologies like DT, cloud IoT platforms, and BDA have transformed the way in which healthcare institutions store, analyse, and utilize data (Khan et al., 2022). Innovations such as hierarchical data fusion and hybrid cloud-IoT models have significantly improved decision-making and real-time patient monitoring (Dautov et al., 2019; Elhoseny et al., 2018). Blockchain has also emerged as a key solution for protecting health records and improving transparency in medical insurance claims and medication adherence (Kumar et al., 2020a, b). Owing to the growing demand for wearable healthcare devices, security measures must be developed to safeguard patient privacy and prevent data breaches (Paul et al., 2021).

This cluster studies how Industry 4.0 improves the healthcare value chain through IoT-enabled monitoring of patient flow, automation of hospital logistics, and blockchain-based tracking of pharmaceutical products.

I4.0 transforms the healthcare value chain by streamlining operations, improving patient care, and reducing waste of resources. IoT is an important enabler in monitoring the flow of patients, automating hospital logistics, and improving access to remote care for underserved populations (Daú et al., 2019; Salih et al., 2022). Intelligent networks that link patients to health professionals and devices improve the management of the supply chain, reduce bottlenecks, and enhance the exchange of information (Al-Jaroodi et al., 2020). The ability of a well-trained workforce to leverage these technologies is crucial, especially in telemedicine, where IoT can facilitate remote diagnosis and patient monitoring (Pang et al., 2018; Popov et al., 2022). Innovations in blockchain technology, such as the use of Non-Fungible Tokens (NFTs) for drug serialization, can help prevent counterfeit medicine and increase transparency in the pharmaceutical system.

This cluster examines how cloud and fog computing contribute to real-time healthcare decision-making and reduce latencies to support scalable and secure medical data access.

Cloud and fog computing are revolutionizing healthcare by enabling scalable, real-time data access while mitigating latency issues. Cloud storage allows seamless medical data sharing, but encryption is necessary to protect sensitive patient records. Fog computing enhances real-time decision-making by processing data closer to the point of care, reducing reliance on centralized cloud servers (Aceto et al., 2020). Privacy-preserving frameworks have been proposed to enhance security in cloud-based healthcare models while maintaining accessibility (Ogundoyin and Kamil, 2021). Multilayered fog-computing architectures support decentralized patient monitoring systems, improving healthcare delivery, particularly in remote areas (Kharel et al., 2017; Kumari et al., 2018).

This cluster illustrates the value of Industry 4.0 technologies, such as AI-based disease tracking and remote monitoring, in addressing the healthcare challenges posed by the COVID-19 pandemic.

The COVID-19 pandemic emphasized the role of I4.0 in promoting resiliency and activity in healthcare during an emergency. These I4.0 technologies, such as AI-based disease tracking, remote access to healthcare, and automated supply chain processes, helped in tackling the disruption of healthcare and the logistical supply of medicine and equipment (Ahsan and Siddique, 2022). In the domain of dentistry, sensor-based innovations and teledentistry enable continued service to patients in spite of physical constraints (Javaid et al., 2021). Additionally, the use of AI-driven predictive modelling and logistics frameworks improved distribution systems for vital medical supplies and resource allocation (Abdel-Basset et al., 2021). Lastly, remote patient management using wearables and smart monitoring technology reduced overcrowding in healthcare facilities and improved healthcare system efficiency during the pandemic.

This cluster examines the role of 3D printing in enabling personalized medicine, customized drug formulations, and precision dosing.

The advances in 3D printing are changing the way drugs are produced in the pharmaceutical industry while allowing for personalized dosing and manufacturing of medicines on demand. Precisely customized formulations tailored to each individual can improve therapeutic effects while decreasing side effects (Ong et al., 2021; Praveena et al., 2022). AI and ML combined with 3D printing can further advance the customization of medications by optimizing drug design using 3D computer-aided models (Xu et al., 2021). Robot-assisted precision in surgical procedures and targeted drug delivery have been used successfully to improve patient outcomes and decrease medical waste (Awad et al., 2021; Chauhan et al., 2021). Other combinations of technologies, such as 3D-printed medication with biosensors, can measure drug absorption in real-time, enabling more patient-centred approaches to treatment (Castro et al., 2021; Seoane-Viaño et al., 2021). The technologies mentioned are now transitioning from experimental phases to mainstream pharmaceutical applications (Elbadawi et al., 2021).

The thematic clusters on I4.0 and healthcare applications highlight an interconnected ecosystem where technological advancements collectively reshape healthcare. A key synergy exists between data management (Cluster 2) and healthcare value chains (Cluster 3), as blockchain enhances data security while ensuring medicine authenticity and efficient logistics.

The adoption of I4.0 (Cluster 1) drives innovations across other clusters, with AI, IoT, and BDA improving operational efficiency and enabling precision in 3D-printed drug dosing (Cluster 6). Cloud and fog computing (Cluster 4) support real-time data sharing and decentralized processing, crucial for remote healthcare services (Clusters 3 and 5), especially during the COVID-19 pandemic.

The challenges posed by the pandemic (Cluster 5) have accelerated I4.0 adoption, emphasizing telemedicine and sensor-based healthcare devices, which integrate data-driven and patient-centric care. The research suggests that I4.0 acts as a unifying framework, optimizing healthcare delivery, accessibility, and resilience through integrated intelligent systems.

The review emphasizes the crucial function of I4.0 technologies in healthcare that consist of BDA, AI and ML, IoT, cloud and fog computing, blockchain, and 3D printing. The results effectively respond to RQ1; I4.0 enable real-time patient monitoring (IoMT, WBANs), AI-enabled diagnostics, and robot-assisted surgery. These technologies enhance hospital operations, streamline supply chains, and enable personalized healthcare (Karatas et al., 2022). 3D printing helps to develop personalized drug formulations and assists with precise dosing while maximizing the effectiveness of treatment (Ong et al., 2021). To address RQ2, the findings highlight how I4.0 technologies have improved patient outcomes, the quality of care, and efficiency in hospital operations. AI-driven predictive diagnostics, precision medicine, and cloud-based technologies assist in better diagnosis, real-time decision-making, and improved patient engagement and management (Kotzias et al., 2022). During the pandemic, I4.0 technologies were widely adopted due to healthcare disruptions, with AI being used considerably for tracking the spread of virus strains, remote monitoring of patients, and automating the supply chain processes (Ahsan and Siddique, 2022).

Notwithstanding these advancements, there are a number of barriers to the adoption of I4.0 into healthcare. Addressing RQ3, barriers to the adoption of I4.0 technologies have been discussed, which include a lack of top management support, skill gaps in the workforce, high cost of implementation and inadequate regulatory frameworks (Ajmera and Jain, 2019; Cavallone and Palumbo, 2020). The interventions may require financial support and training programs to bridge the gaps that already exist. Furthermore, concerns continue to surround patient data privacy and security. In terms of RQ4, blockchain, federated learning, and encryption technologies play a significant role in securing patient data and addressing transparency, data integrity and patient consent management (Kumar et al., 2020a, b; Ogundoyin and Kamil, 2021). However, there remain cybersecurity risks, which demand stronger encryption methods and data protection regulations. It is essential to address these barriers through policy support, infrastructure changes and training of the workforce to ensure full realisation of the benefits of I4.0 in healthcare.

A successful deployment of I4.0 in healthcare requires a strategic and multi-layered approach which considers the technological readiness, impact of technology, barriers and enablers, and security and ethical concerns, as presented in Figure 5.

A reliable digital health infrastructure is essential to real-time surveillance, predictive analytics, and data transfer engagement. The integration of advanced technologies, including IoT, AI, blockchain, cloud computing, and BDA, enables automated diagnostics, remote patient monitoring, workflow optimization, and improved decision-making. Nevertheless, access to technology is not enough—considerations of scalability, interoperability, and how it can integrate with existing healthcare IT have to be prioritized for sustainment to take place.

Evaluating the efficacy of I4.0 technologies is essential for building trust in their potential impact on patient outcomes, quality of care, and operational performance. Examples of performance metrics include diagnostic accuracy, patient engagement, flow and process efficiency, and quality of care. Continuous monitoring and feedback are necessary to identify gaps, refine technologies, and enhance healthcare operations. A data-based approach helps to ensure that healthcare workers benefit from technology in a patient-centred healthcare model.

Adoption of I4.0 technologies in healthcare is often limited due to regulatory challenges, high cost of implementation, interoperability issues, and lack of skilled workforce. Addressing these barriers requires appropriate policies to support adoption, funding for implementation, and workforce training while ensuring that interoperability standards are established across systems. Change management strategies are necessary to ensure stakeholder buy-in and to create a culture of digital transformation within healthcare settings.

With the increasing digitization of healthcare, data security, patient anonymity and confidentiality, and ethical AI deployment must be at the forefront of technology adoption. Utilizing blockchain-enabled health records, strong cybersecurity protocols, and adherence to regulations like GDPR, data integrity and patient trust can be established. AI ethics frameworks should promote transparency and unbiased decisions in automated healthcare applications.

By considering these fundamental layers, healthcare organizations can effectively implement I4.0, leading to enhanced efficiency, improved patient care, and sustainable digital transformation. The differences in infrastructure, policy, and regulations across regions would all greatly impact the degree to which Industry 4.0 technologies can be successfully adopted in healthcare settings. Issues like a lack of regulatory frameworks, inadequate IT infrastructure and an unskilled workforce impede operationalization (Ajmera and Jain, 2019). These differences necessitate context-specific approaches for deployment and regulatory alignment for sustaining I4.0 in health care.

In comparison to existing literature, this study presents a broader discussion on healthcare data management, aligning with work by Khan et al. (2022) and Dautov et al. (2019) while emphasizing the need for ethical considerations in data handling. The current study consolidates the challenges to the adoption of I4.0 in healthcare across multiple thematic clusters, offering a more comprehensive perspective. While previous works have drawn attention to technological approaches (Javaid and Haleem, 2020) or perspectives on data security (Kumar et al., 2020a, b), this study bridges the gap by discussing both implementation challenges and enablers, ensuring a balanced perspective while taking into consideration healthcare practitioners and policymakers for patient-centric care.

The outcomes of this study have important theoretical implications for scholars and researchers who wish to study the role of I4.0 in healthcare. This research contributes to the wider body of knowledge by providing insights into how I4.0 technologies are being incorporated into healthcare, including their varied applications and quantifiable impact on patient outcomes and the delivery of healthcare. The study contributes to Institutional Theory and Resource-Based View (RBV) by demonstrating how I4.0 technologies can be viewed as strategic resources that provide competitive advantage and operational resilience to healthcare systems. In addition, the findings expand the Technology Acceptance Model (TAM) by demonstrating the barriers to adoption, including regulatory barriers and organisational barriers. This expanded understanding of the TAM will enable new pathways for innovation within healthcare contexts. The study extends digital transformation theories by integrating the socio-technical aspects of I4.0, emphasizing the interplay between innovation, regulation, and adoption.

The findings of the study can help policymakers make strategic decisions to integrate I4.0 into healthcare infrastructure successfully. Understanding the prominent themes and challenges highlighted in the literature may guide governments to formulate policies that support the responsible adoption of these technologies. Barriers such as skill gaps and infrastructure limitations call for targeted training programs and strategic investments in the healthcare industry. Fostering collaboration between engineers and healthcare professionals requires joint research initiatives, industry-academic partnerships, and integrated training programs. Establishing interdisciplinary innovation hubs and incorporating Industry 4.0 concepts into medical and engineering education can enhance such cooperation. Furthermore, the evidence on data privacy and security highlights the importance of implementing secure data management frameworks to build patient trust and ensure compliance with regulatory standards. By addressing these challenges, stakeholders can accelerate the adoption of I4.0, leading to more efficient and secure healthcare systems. The proposed framework for deploying I4.0 in healthcare provides a structured pathway for hospitals, pharmaceutical firms, and regulatory bodies to deal better with the challenges of I4.0 integration.

This study provides a comprehensive evaluation of I4.0 applications in healthcare, highlighting its significant potential in addressing the growing demand for efficient healthcare solutions. Bibliometric analysis identified key thematic clusters, top contributing authors, institutions, countries and sources, offering insights into the current research landscape. These clusters span diverse areas, including I4.0 adoption, data management, healthcare value chains, cloud and fog computing, pandemic-driven implementations, and 3D printing of medicines.

While the proposed framework and thematic clusters provide a thorough understanding of I4.0 in healthcare, several limitations remain, leaving scope for future research. Reliance on a single database – Scopus, the current study might have left relevant studies from other databases like Web of Science, PubMed, and Google Scholar, introducing potential bias. Expanding data sources would provide a more comprehensive and inclusive understanding of the global research landscape.

This study identifies thematic trends and proposes a framework for I4.0 adoption, but lacks empirical analysis of real-world challenges. Institutional strategies across different economic and regulatory settings can be examined to assess the impact on healthcare professionals and patient engagement. Future research should prioritize the legal, ethical, and socio-political aspects of I4.0 research, specifically how governance structures can enable innovation while ensuring the rights of the patient and their data safety. The field of research can benefit further from longitudinal studies that assess the long-term implications of AI, IoT, blockchain, and automation on patient outcomes, costs, and system resilience. It will be key to understand how these technologies mature and merge within healthcare systems, as this will shape future policy for advanced healthcare delivery models. Developing standardized interoperability frameworks will further ensure seamless integration of these technologies across multiple disciplines.