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Abstract

Industry 5.0 is reshaping healthcare through human-centric design, sustainability, and advanced technologies. However, there is limited insight into how these innovations address the specific needs of people with disabilities. This review aims to examine the role of emerging and AI-driven technologies in enabling disability-inclusive digital healthcare solutions. A comprehensive scoping review was conducted, focusing on studies published in recent years on Industry 5.0 technologies applied to disability-inclusive digital healthcare pathways. Key technologies reviewed include collaborative robotics, virtual reality, telemedicine, and human-centered artificial intelligence. Relevant case studies and ethical considerations were also analysed. The analysis highlighted that Industry 5.0 technologies show promise in enhancing diagnostic accuracy, personalization, and accessibility for people with disabilities. Applications include remote assessments, assistive tools, and adaptive interfaces that improve diagnostic processes. Despite this progress, integration of these technologies remains fragmented, and challenges such as ethical concerns, regulatory barriers, and inclusive design persist. This review uniquely synthesizes these technologies within the framework of Industry 5.0, offering a broader perspective than prior single-technology reviews and proposing a roadmap for the successful implementation that incorporates training, regulatory alignment, interdisciplinary collaboration, social-economic barriers, real-world evidence, and inclusivity across disability types. As conclusion, Industry 5.0 holds significant promise for advancing disability-inclusive digital healthcare. Realizing this potential, however, requires coordinated efforts to address integration gaps, strengthen ethical and regulatory frameworks, and embed user-centered co-design principles. Future research should focus on more developing inclusive, and sustainable diagnostic solutions aligned with Industry 5.0 principles.

Keywords

Industry 5.0 artificial intelligence disability-inclusive diagnostics virtual reality digital solutions

Introduction

Industry 4.0 (IR 4.0) integrates technologies like the Internet of Things (IOT), big data, artificial intelligence (AI), and automation into industrial processes.¹ The main objectives of IR 4.0 adoption are to enhance efficiency, productivity, and automation by using data-driven insights and connected systems. Smart factory and Cyber Physical Production System (CPPS) are the core elements of IR 4.0 in an industrial setting.² IR 4.0 is not typically regarded as a human-focused initiative, and it is also known as a human out of the loop transformation.² A new paradigm for a manufacturing environment free from human intervention, driven by product intelligence, machine-to-machine communication, and networking, is promoted through IR 4.0 implementation.³ IR 4.0 are often criticized for prioritizing efficiency over environmental and social impacts, where sustainability is not the core focus but occasionally considered to reduce cost, especially in terms of improving resource efficiency.⁴

Meanwhile, Industry 5.0 (IR 5.0) emerged as an extension to IR 4.0, incorporating social and environmental dimensions. IR 5.0 emphasizes worker skills, knowledge, and their ability to collaborate with robots and machines while focusing on flexibility in the production process and minimizing environmental impact.^5,6 IR 5.0 reinforces the co-existence of humans and machines, ensuring automation support for human creativity and customization.⁷ Human positioned as partners to machine, focusing on personalization and problem-solving. The key technologies of IR 5.0 are collaborative robots, AI-driven personal assistance, customization-focused systems and techniques enabling sustainability and green technologies.^8,9 It is represented by concepts such as sustainability, human-centric values, quality of life enhancement, and mass personalization.¹⁰

Unlike IR 4.0, which emphasized automation and efficiency, IR 5.0 shifts toward human-centric innovation, making social inclusion, personalization, and the well-being of people at the peak of technological progress. This paradigm directly supports disability-inclusive healthcare, which integrates collaborative robotics, explainable AI, and assistive digital technologies, empowering patients and caregivers as active actors in the care process. Besides, ethical AI and human–machine co-evolution guarantee that healthcare solutions are designed around users’ needs, reducing, as a consequence, barriers in diagnosis, daily functioning, and rehabilitation globally. Therefore, the social-responsibility pillars of IR 5.0 encourage inclusive policy Frameworks, which can establish a good alignment between industrial innovation and the United Nations’ Sustainable Development Goals (SDGs 10 and 3) and reduce as a consequence inequalities.¹¹ In this context, IR 5.0 provides a transformative pathway for the development of healthcare ecosystems, not only technologically advanced, but also value driven-road, accessible, equitable, and responsive to the needs of people with disabilities. Although IR 4.0 supports certain aspects of sustainability at the micro-organizational level, it causes employment disruption, loss of workplace autonomy, the digital divide, social exclusion, overconsumption, and uneven regional development.¹² The European Commission, in a policy brief, stated that IR 4.0 is no longer an adequate framework for achieving sustainable industrial transformation or addressing modern socio-environmental challenges. IR 4.0 falls short in incorporating circulatory into value chains, fostering well-being, and preventing environmental degradation.¹³ The key reasons for the transformation from IR 4.0 to IR 5.0 are shown in Figure 1. Despite the growing discourse around the transition from IR 4.0 to IR 5.0, a major conceptual and empirical gap concerning how this transition translates into disability-inclusive healthcare innovation has still not been deeply highlighted. Besides, a very limited number of researchers have synthesized how the human-centric, sustainable, and collaborative values of IR 5.0 can systematically advance health equity for disabled people despite the effort to review the individual technologies' contribution to this topic, such as telemedicine, AI, or robotics. Moreover, the interaction of these technologies with inclusive design principles, regulatory frameworks, and ethical challenges in real-world healthcare delivery is often overlooked by current research. Guided by these considerations, this review seeks to discuss the following issues: (i) How does the transition to IR 5.0 transform digital healthcare toward greater inclusion of disabled people? (ii) Which emergent and AI-driven technologies are currently enabling disability-inclusive diagnostics, treatment, and rehabilitation? (iii) What are the key challenges, ethical considerations, and policy implications involved in operationalizing IR 5.0 principles within healthcare systems?

Figure 1.

Transformation from IR 4.0 to IR 5.0.

Analytically, this review is guided by the IR 5.0 human-centric innovation framework, requiring reinforcement of social inclusion, sustainability, and resilience for its flow process. This theoretical lens provides the basis for interpreting the transformation of disability-inclusive healthcare systems by AI-driven and emergent technologies. Therefore, the main objective of this article is to systematically examine and integrate current knowledge on the use of emergent and AI technologies for disability-inclusive digital healthcare within the framework of IR 5.0. In doing so, this review seeks to bridge the gap between technological innovation, in field practices and social inclusions, offering a conceptual roadmap for designing equitable, human-centered, and sustainable healthcare ecosystems.

Materials and methods

This study adopted a scoping review design, following the framework proposed by Arksey and O’Malley¹⁴ and enhanced by the Joanna Briggs Institute (JBI) for scoping reviews. The scoping approach was selected as it allows the mapping of a broad, interdisciplinary entity that spans emerging technologies, disability inclusion, and healthcare transformation under the IR 5.0 paradigm, fields and topics that are conceptually diverse and complex, and methodologically heterogeneous and multiscale. The review was conducted in alignment with the PRISMA-ScR guidelines to ensure transparency and reproducibility. A formal protocol was developed internally by the research team and applied throughout the research work. However, given the exploratory nature of the topic, it was not registered in PROSPERO, which primarily indexes systematic reviews of clinical interventions. The flow process was conducted in PubMed, Scopus, Web of Science, and IEEE Xplore databases in December 2024. The search combined key terms and Boolean operators as follows: (Industry 5.0 or Human Centric Industry) and (Artificial Intelligence or AI) and (disability or Disability Inclusion or Assistive Technology) and (healthcare or Digital Health or Diagnostics or rehabilitation). Reference lists of key papers as well as relevant reviews were also screened to identify and confirm additional sources. The search was limited to English language publications, from 2015 to 2024, to concentrate on contemporary development in IR 5.0 and emergent digital health technologies. The studies were included when they covering the following criteria: (i) discussed or applied IR 5.0 related technologies (AI, robotics, VR, AR, etc.) in the context of disability-inclusive healthcare, (ii) presented case studies, reviews, empirical investigation, or conceptual frameworks related to the global topic (inclusion, accessibility, or human centric healthcare design), (iii) published in peer-reviewed journals or confirmed conference proceedings. When the studies were focused solely on IR 4.0 or general digital transformation without a healthcare or disability focus, or the source of the information was from non-peer-reviewed materials (opinion pieces, editorial, etc.), the references were excluded. The data extraction framework is detailed in Table 1, in which the different information discussed and evaluated by our teams and other benevolent experts are summarized, such as publication year, country, type of technology, disability category, healthcare application, outcomes, challenges, and limitations. Collected available data were then synthesized narratively and thematically to identify recurring patterns, conceptual gaps across studies, technological trends, and the degree of evolution and incorporation of the characteristics of IR 5.0 in the different healthcare ecosystems. The screening and selection process followed PRISMA-ScR guidelines and is reflected and summarized in the results narrative, indicating the total number of the identified, screened, excluded, and finally retained records for full analysis. Five analytics themes were addressed: (i) the importance of disability inclusion in healthcare, (ii) types and impact of disabilities, (iii) emergent technologies and diagnostics, (iv) role of AI in disability diagnostics and care approaches and strategies, and (v) illustrative case studies and success stories. In front of some limitations such as the heterogeneity of study designs and outcomes measures, and absence of standardized quality appraisal tools, several measures were adapted and well applied: (i) a multi-stage screening and review consensus process to enhance reliability in study selection, (ii) refinement of search terms in collaboration with subject experts in the related field and topic, (iii) inclusion of both conceptual and empirical evidence, recognizing the emergent nature of IR 5.0 and its applications to inclusive healthcare.

Table 1.

Data extraction framework.

Variable	Description/Extracted data
Publication year	Year in which the study was published; used to track temporal trends in research on Industry 5.0 and disability-inclusive healthcare
Country/Region	Geographic origin or study setting helps identify the regional focus and distribution of research efforts
Type of technology	Specifies the emergent or AI-driven technology discussed or applied (e.g., AI, robotics, VR/AR, 3D printing, telemedicine, wearable sensors)
Disability category	Identifies the type of disability addressed (e.g., physical, sensory, cognitive, developmental, or multiple disabilities)
Healthcare application/context	Defines the primary healthcare domain in which the technology is applied (e.g., diagnostics, treatment, rehabilitation, assistive communication)
Methodology/study type	Indicates whether the source is empirical, conceptual, a review, or a case study
Outcomes and key findings	Summarizes the main contributions, measured impacts, or technological benefits related to disability inclusion and healthcare improvement
Ethical/policy implications	Notes issues related to accessibility, inclusivity, ethics, or regulatory frameworks discussed in the study
Limitations	Reports methodological, contextual, or data-related limitations mentioned by the authors

Results and discussion

The results of the scoping review were organized into five thematic categories derived from the data extraction framework: Health inequalities and inclusion needs, (ii) disability typologies and their impact on healthcare access, (iii) emergent technologies enabling inclusive diagnostics and rehabilitation, (iv) AI and personalized care, (v) success stories and in-field case studies, illustrating IR 5.0 paradigm. A progressive synthesis- from conceptual needs and challenges to the technological and systemic innovations through the principles of IR 5.0 was structured in this section of results and discussion.

Importance of disability inclusion in healthcare

Access to healthcare is essential for achieving good health and is recognized as a fundamental human right. An inclusive healthcare system for people with disabilities is essential to ensure equitable access to medical services, improve health outcomes, and promote dignity and social justice. It is estimated by the World Health Organization (WHO) that around one billion people globally live with a disability, with approximately 80% residing in low and middle-income countries.¹⁵ Studies have shown that people with disabilities, whether in high-income countries or low- and middle-income countries, are more likely to have poorer health outcomes compared to those without disabilities.¹⁶ Disabilities can be physical, sensory, intellectual, or mental health related. The World Health Survey estimates that the prevalence of disability is 60% higher among women compared to men.¹⁷ Poor access to healthcare for people with disabilities has a far-reaching impact on their health, well-being, and social participation. The key consequences of poor access to healthcare are presented in Figure 2. The United Nations International Children Emergency Fund (UNICET) reported that children with disabilities account for approximately 150 million. These children have less access to the healthcare system due to few reasons, such as personal and community beliefs, lack of awareness of disability, unavailability of health services, complicated clinical pathways, and absence of qualified health professionals.^18–20

Figure 2.

Key consequences of poor access to healthcare.

IR 4.0 technologies have significantly enhanced the inclusiveness of the healthcare system for people with disabilities by addressing barriers, improving accessibility, and enabling personal care. Advanced data analysis for personalized care can analyze health records and behavior patterns to design personalized treatment plans for individuals with disabilities. However, existing personalized healthcare is tailored to a specific environment, focusing on technological customization through devices and gadgets. They often overlook the complexity of interconnected health conditions, leading to misdiagnosis and compromising both sustainability and long-term health and quality of life for patients.²¹ One of the major elements of IR 5.0 is human-centered artificial intelligence (HCAI). Facilitation of human–machine interaction is crucial, and it focuses on designing AI systems that are user-friendly, comprehensible, and accessible, bridging the gap and enhancing the way humans engage with technology.²² While IR 4.0 relies much on autonomous robotics and efficiency, it often sidelines human aspects. Patients and healthcare workers are treated more as elements of components than as individuals with unique needs. Advantages of Industry 5.0 over Industry 4.0 in providing healthcare for people with disabilities are listed in Table 2.

Table 2.

Advantages of Industry 5.0 over Industry 4.0 in providing healthcare for people with disabilities.

Elements	Examples
Human-centric design emphasizing the importance of addressing individual's complex needs, preferences, behaviors, and interactions while involving marginalized and underrepresented communities	Human-centered design is applied by Altiparmakogulari et al. in dental photography. Earlier research primarily concentrated on equipment but in human centered design approach, a holistic review is taken, examining the entire system involving both equipment and people. It incorporated ergonomics, emotional impact on patients, and human factors ²³
	Meredith et al. proposed human centered, Evidence-Driven Adaptive Design (AHEAD) to improve industry in healthcare ²⁴
Collaborative robots (Cobots) work alongside healthcare professional and patients to provide real-time assistance tailored to individual needs	Development of assistive robotics for healthcare and rehabilitation of people disabilities²⁵
	Hussain et al. reported the functions of cobots in enhancing hospital management. The cobots serve as an interface between patients and medical professionals and allow patients to participate in therapy and educational sessions²⁶
Personal healthcare solution through the implementation of advanced AI and data-driven insights	Rehabilitation process can benefit from AI technologies, such as the application of customized and assisted rehabilitation treatment for neurological and developmental disorders. Virtual reality (VR) based rehabilitation and care for neurodegenerative conditions is also useful²⁷
	AI chatbots and VR platforms have demonstrated significant potential in delivering VR and creating a secure space for patients to share their feelings and thoughts. These innovations enable effective therapeutic support while eliminating geographical constraints. The technology improves accessibility to therapy for disabled patients^28,29
Integration of smart sensors, AI, and wearable devices to enhance the quality of life for disabled individuals	Google Glass a prototype therapeutic tool was developed for autistic children to show correct emotional classification to the children to enhance their emotion identification capabilities^30,31
	Advantages in computer vision have become a game changer for AI-powered assistance, enabling individual with visual impairment to interact with or more through their environment by leveraging knowledge on object recognition, large data analysis, and scene interpretation. AI applications provide a description of boundary, detect barriers, and facilitate independent^32,33
Inclusivity through Industry 5.0. AI systems designed to avoid bias and ensure inclusivity, addressing accessibility barriers	In 2019, during the coronavirus disease AI system was found to increase inequalities due to bias and increase the risk of severe illness and death among people with disabilities³⁴
	Participant-centered development of AI algorithms must engage members of underrepresented groups to increase advocacy for people with disabilities³⁵
	Platforms such as Open Human permit participants to input their personal information, create exclusive research questions, and create and share exclusive algorithms³⁴

Disabilities and impact on healthcare

Disabilities are defined as physical, mental, intellectual, or sensory issues that constrain human ability to carry out activities of daily living, participate as a part of the community with meaningful societal roles, or grab opportunities in terms of academic and career.³⁶ In recent years, patients, especially children with intellectual or sensory disorders, have increased dramatically, which will cause a greater impact on the healthcare system.³⁷ The categories of disabilities are shown in Figure 3. The impact of disabilities on healthcare, as reported in some previous literature, is listed in Table 3. Individuals with disabilities face numerous barriers when accessing healthcare, as summarized in Figure 4. The impact of the barriers on the disabled patients is shown in Figure 5.

Figure 3.

Type of disabilities.

Figure 4.

Individuals with disabilities face numerous barriers when accessing healthcare.

Figure 5.

The impact of the barriers on disabled patients.

Table 3.

Impact of disabilities on healthcare.

Impact on healthcare	Reported studies	Refs.
Increase in other healthcare needs for people with disabilities	Higher ophthalmic health is needed for children with disabilities, especially children with facial disability and coordination issues	Pu and Hou³⁸
	Studies by Hwang et al. show that the prevalence of diabetes for all age groups in the South Korean population with disabilities is double compared to the population without disabilities. One of the reasons for this occurrence is that patients with disabilities do not undergo early tests and medical checkups to prevent diabetes	Hwang et al.³⁹
	In the United States, 34.7% of stone formers and 16.1% of nephrolithiasis patients are people with disabilities	Elia et al.⁴⁰
Barrier to access healthcare services, including physical, communication, financial, and attitudinal barriers	Lack of information and clear pathways, limited resources of mental health services, inefficient pre-admission and admission process, unambiguous communication between parents and healthcare professionals, and stigma and culture are presented as main barriers to access to healthcare for children with autism in the UK	Babalola et al.⁴¹
	In another study by Star et al., most of the children and adults with neurodevelopmental disorders receive routine dental care with general anesthesia (GA), which is very costly. A survey shows that only 66.4% of patients returned for the treatment after the first treatment with GA	Star et al.⁴²
	Women with disabilities face a greater likelihood of being diagnosed with advanced-stage breast cancer and experiencing a higher mortality rate due to limited access to widespread screening. These individuals often encounter difficulties in obtaining breast-imaging services because many imaging centers lack adequate facilities. The staff are not well trained to meet the needs of disabled patients	Net et al.⁴³
Health inequities	Data published by the National Institute of Health Survey (NIHS) in the United States shows that health inequities among people with disabilities vary in accordance with race and gender. Adults with disabilities from Black and Latino communities face more health disparities compared to their White peers. Women generally face more disparities compared to men	Mitra et al.⁴⁴
Health inequities	In a study conducted by Ona et al., patients with spinal cord injury (SCI) from 22 countries were studied to relate income and comorbidity index. Patients with SCI in the lower-income group reported four times more comorbidities compared to patients in the higher-income group	Ona et al.⁴⁵
Psychological impact	A study by Manning et al., post-COVID-19 pandemic, shows that 50% of adults in the United States with disabilities experience depression and anxiety disorders during the pandemic	Wang et al.⁴⁶
	A study was conducted on 260 children from special education schools. 60.9% children with intellectual disabilities showed early identification of mental health problems compared to only 9.8% of children without intellectual disabilities	Kaptein et al.⁴⁷
	Based on a survey done on 72 people with different disabilities in South Africa, barriers to employment are still prevalent. Failure to secure a job further affects access to healthcare services due to financial issues	Moscoso-Porras et al.⁴⁸

Emergent technologies and disability diagnostics

Emergent technologies in healthcare are shaping the future of medical treatments, patient care, diagnostics, and health management. These innovations are driven by advancements in fields such as AI, biotechnologyrobotics and data analytics. Although technologies such as telemedicine, augmented reality, and virtual reality originated under IR 4.0, their integration within the IR5.0 framework marks a paradigm shift from automation and care efficiency to human-centered co-creation and application. In this context, these technologies are considered not merely as tools but also collaborative enablers, permitting enhanced accessibility, personalization, and sustainability in disability inclusive healthcare.

Telemedicine and remote patient monitoring

Remote patient monitoring (RPM) utilizes digital technologies to track and monitor patients’ health conditions in real time, enabling healthcare professionals to collect vital health data from patients while they remain outside a conventional clinical environment.^49,50 Telemedicine enables remote healthcare services, including diagnosis, consultation, and treatment, through the use of communication technologies.⁵¹ Combined with tools like wearables monitoring devices, it offers a pathway to more tailored, effective, and real-time medical care. The integration of big data significantly enhances both telemedicine and RPM by facilitating the gathering, analysis, and interpretation of extensive health-related information.

For instance, web-based monitoring of patients with inflammatory bowel disease (IBD) has been shown to enhance patients’ quality of life, improve adherence to prescribed medicine, and reduce healthcare expenses. The patients could also report symptoms and notify the medical team of their condition in real time. These digital tools enable timely treatment for patients without requiring them to visit health facilities.⁵² Atilgan et al. had conducted RPM from 2017 to 2020 on 2340 patients who had undergone cardiac surgery. Medical devices, namely Minotr DakiApp and Holter EG, were used to register crucial parameters such as pulse rate, blood pressure, body temperature, electrocardiography, and blood glucose. These data were kept in web-based software and mobile applications, which then enable Remote Medical Evaluation (RME). According to the statistics, the Telemedicine team had handled 79,560 RMEs between 2017 and 2020. 144 patients were hospitalized and underwent appropriate treatments due to mild and potentially life-threatening complications detected by RME.⁵³ Telemedicare and RPM had become very crucial for screening, diagnosis, and monitoring COVID-19 pandemic patients.⁵⁴ Continuous monitoring of COVID-19 patients with continuous pulse oximetry has resulted in a mortality rate of 6 per 1000 patients compared to 26 per 1000 patients without RPM. Pulse oximeters, traditionally used in hospitals and at home. It transmits the data to smartphones, secure cloud platforms, and web dashboards. This enables physicians and healthcare providers to monitor patients’ conditions in real time.⁵⁵ The success of RPM and telemedicine depends largely on the continuous and frequent engagement of patients with the technology. A case study on 1354 diabetes type two patients between 2015 and 2017 showed that patients who uploaded an average one upload of data per day had lower hemoglobin Alc (HbAlc) compared to patients who registered but did not frequently upload their data.⁵⁶ The success of RPM and telemedicine also depends on effective and user-friendly telemedicine tools. Application of nanotechnology enabled telemedicine sensor development for COVID-19 diagnosis, including a wearable sensor to monitor vital signs and metabolic sensors. Web-based platforms and smartphone apps were proven to be effective in the management of COVID-19 patients as reported by Lukas et al.⁵⁶ Even though, in principle, telemedicine could be very beneficial for patients in remote area but the implementation and its success depend largely on factors such as internet connectivity problems.⁵⁷ From this global analysis, it can be concluded that in the context of IR 5.0, telemedicine involves beyond digital efficiency to embody human-centered continuity of care, ensuring that intelligent monitoring systems not only automate diagnostics, but also extend accessibility, empathy, and inclusion for persons with disabilities, across geographic and socio-economic barriers.

Wearable health technology

The early stages of wearable health device development were noted between the 1960s and 1990s, with the development of basic heart rate monitors and portable ECGs. The technology bloomed around 2014 with the introduction of fitness trackers and smartwatches.⁵⁸ In 2020, 19% of Americans utilized wearable fitness trackers.^59,60 At the same time, between 2005 and 2010, smart inhalers and other connected devices for asthma monitoring gained popularity in terms of usage among consumers. In 2012, Propeller Health introduced a smart inhaler device, which uses a sensor to monitor the frequency of inhaler use and record the data in mobile apps.⁶¹ The development of wearable device technology in healthcare is shown in Figure 6.

Figure 6.

Timeline of wearable health technology development.

For the design of inclusive wearable devices, it must consider diverse disability types. In fact, for individuals with mobility impairment, lightweight and flexible materials facilitate ease of wearing and removal. Whereas haptic or auditory cues can replace visual displays for users with visual impairments, while AI-enabled adaptive feedback allows cognitive or neurological disability users to receive simplified, context-aware notifications. As a consequence, such technology transforms wearable devices from mere monitoring tools into truly inclusive healthcare companions. In terms of size and ergonomic characteristics, wearable devices are small electronic gadgets or computers equipped with wireless communication capabilities. These devices are integrated into items such as accessories, clothing, or even more invasive forms of technology as microchips or smart tattoos that can be worn on the body. Unlike conventional smartphones and tablets, wearable offers the added benefits of continuous monitoring and data saving features such as biofeedback and physiological functions released by biometrics. Despite their battery limitations, wearables are compact, portable, convenient, and provide hands-free interaction with electronic systems.⁶² The market growth rate for wearables is expected to increase by 20% annually and will reach more than 150 billion euros by 2028.⁶³ The COVID-19 pandemic has accelerated the spread of wearable technology.¹⁴ However, the development of wearable health device technology is quite slow compared to other hardware technologies like smartphones and tablets due to low acceptance among consumers.⁶⁴ The development of a useful and generally accepted wearable health technology requires collaborations between researchers, software developers, clinicians, statisticians, and information technologists.⁶⁵ Therefore, it can be concluded that, in IR 5.0, wearable technologies transform from passive monitoring devices into interactive companions that can adapt to users’ physical, sensory, and cognitive abilities, supporting personalization, empathy, and well-being of disabled people through continuous human–machine collaboration. Examples of wearable health technology devices are summarized in Table 4.

Table 4.

Examples of wearable health technology devices.

Monitoring	Device technology	Health data collected	Ref.
Acoustic gastrointestinal biosensors	Real-time biosensor marker of postoperative intestinal motility	Sensors allow continuous and automated analysis of bowel sound	Liang et al.⁶⁶
Heat-related illness monitoring among farmers in the Hispanic community	Chest strapped multi-parameter wearable tracking body temperature, heart rate, and breathing rate	Farmers under uncomfortable climate conditions have a higher mean heart and breathing rate	Rajkomar et al.⁶⁷
Sweat chloride test for cystic fibrosis targeted drug response	Use a wearable sensor with an integrated salt bridge for real-time tracking of sweat chloride concentration. Sensors attached to the arm	Studies on 20 individuals’ show that the mean detection time recorded as 8.3 ± 2.9 min compared to 30 min for conventional sweat kit	Obermeyer and Mullainathan⁶⁸
Atrial fibrillation detection using a cardio tracker	Ring-type wearable devices to detect continuous photo plethysmography (PPG) and analyze the data using deep learning. PPG is a bio signal used to monitor atrial fibrillation	100 participants were used for the collection of the 1303830 PPG sample set. The diagnostic accuracy was recorded at 94.7%	Topol⁶⁹
Smartwatch assessment to identify atrial fibrillation	Irregular pulse among heart patients is detected using optical sensors on wearable devices in smartwatches and monitored using smartphone Apps. If an irregular algorithm is observed, telemedicine will be conducted, a visit is arranged, and an ECG patch provided to the patients	97.5% confidence intervals for atrial fibrillation in participants 65 years and above recorded in this study	Kumar and Saini⁷⁰
Wearable sensors for cancer patients	Bracelet installed with a sensor and vital activities are monitored using smartphone Apps. Study conducted on 30 patients and their vital signs recorded. 83% of the participants completed 12 weeks of follow-ups. 73% of them also completed visual analysis scale (VAS) for pain	Reduce the need for frequent unplanned readmission or emergency visits by 17% and 30% among the participants	Li and Chen⁷¹

Virtual reality (VR) and augmented reality (AR)

Virtual reality (VR) and augmented reality (AR) are considered immersive technologies that have evolved from visualization and training tools of IR 4.0 into human-in-the-loop therapeutic systems characteristics of IR 5.0.^72–74 With this paradigm, VR generates a fully computer-created environment where users interact through head-mounted displays and motion sensors,^75–77 while AR overlays digital information on the physical world through smart glasses or mobile devices.^58,78,79 In fact, within IR 5.0, these technologies transcend their original efficiency-driven purpose to foster personalization, inclusive, and cognitive-emotional engagement in the healthcare of disabled people. These technologies were initially deployed to improve surgical precision and professional training.⁵⁹ From the feedback of use and application, their integration with AI and adaptive interface design was considered a real and concrete evolution that enables co-creation between humans and intelligent systems applied for disability assistive technology.^60–62 Mohapatra and Singh⁶³ reported that these systems dynamically adapt to patient performance, comfort, and therapeutic progress, rather than replacing clinical expertise where it was applied in the field of motor disability. Liang et al.⁶⁶ affirmed this idea when they concluded that these technologies can embody the human-centric ethos of IR 5.0 by embedding AI-driven feedback, special audio, and gesture recognition: it was highlighted some cases in which AR/VR applications were interpreted as individual motor, cognitive, and sensory capabilities, ensuring that therapy sessions involve responsiveness with the user's needs.^67,68 As examples, Leap Motion-based rehabilitation games, which employ motion tracking to assist upper-limb motor recovery, were evaluated as an innovative solution, providing real-time feedback that adjusts exercise difficulty and visual cues based on patient progress.^69,70 Similarly, hologram-driven balance physiotherapy systems, which integrate head-mounted holographic interfaces and AI algorithm, were used as assistive healthcare systems to guide patients through customized balance exercises.^71,80 It was noticed by several experts that these immersive environments transform repetitive rehabilitation tasks into engaging, gamified experiences that enhance motivation and adherence to treatment. In other words, it was highlighted that a possible contribution to these technologies to multisensory inclusivity.^81,82 The incorporation of special audio cues aids disabled users with visual impairments, while haptic feedback mechanisms assist individuals with limited motor control. As a consequence, perception and special awareness can be improved. Such designs illustrate, as a consequence, the shift from technology-centric optimization to human-adaptive co-design, where accessibility, empathy, and comfort drive the innovative rehabilitation process.⁸³ Based on this framework, VR and AR are considered a collaborative environment where patients, clinicians, and AI systems engage interactively to achieve shared rehabilitation goals, well designed for people with disabilities. Creed et al.⁸⁴ considered that VR and AR can successfully embody IR 5.0's vision of co-creation, personalization, and sustainable well-being for disabled people when they couple immersive experiences with human oversight and ethical AI design.

Collaborative robots in rehabilitation and daily care

Collaborative robots (cobots) represent a key pillar of IR 5.0, which can enable safe, adaptive, and human-centered physical interaction between humans and robots in a healthcare ecosystem.^25,26,85 They are designed to work alongside patients and clinicians when they are equipped with force and proximity sensors that ensure safety and responsiveness to human movement.^86,87 In the field of disability rehabilitation, it was reported several applications: the support of cobots to motor recovery and upper-limb training for patients with stroke, cerebral palsy, and spinal cord injury.⁸⁸ Practically, robotic arms can provide assist-as-needed therapy, which adjusts resistance and trajectory based on patient effort and progress.²⁷ When they are combined with AI-driven feedback and biomedical sensing, these robots can personalize therapy intensity and encourage patient motivation through real-time feedback and gamification. It was reported by Tung et al.⁸⁹ that cobots showed successful results in daily care when they were used to assist individuals with limited mobility in performing activities of daily living such as feeding, dressing, or transferring from bed to wheelchair. Studies highlighted that integrating cobots with computer vision and natural language interfaces allows users with physical or speech impairments to command assistive robots.^90,91 This can be achieved through gestures or voice, enhancing autonomy and safety at home or clinical settings. In other contexts and situations, collaborative robots were used to alleviate caregiver burden by performing repetitive tasks like lifting, reducing musculoskeletal strain among healthcare workers.⁹² Therefore, it can be concluded that cobots epitomize IR 5.0 by merging precision with compassion-supporting human caregivers rather than replacing them, which can enable safe physical interaction. And promote dignity, autonomy, and shared intelligence in disability-inclusive healthcare.

Role of artificial intelligence in disability diagnostics

AI in diagnostics and treatment

Artificial intelligence (AI) is increasingly transforming diagnostics and therapeutic interventions for individuals with disabilities by enabling earlier detection, more precise classification of complex conditions, and data-driven treatment optimization. AI systems, especially those based on machine learning and deep neural networks, excel at processing large-scale, heterogeneous datasets such as medical imaging, electronic health records, genetic profiles, and real-time biosensor inputs.^93,94 For example, AI-based image analysis tools can detect subtle anomalies in MRI or CT scans for conditions such as neurodevelopmental disorders, cerebral palsy, or visual impairments that might escape human observation.^95,96 AI-powered diagnostic tools like Canvas Dx significantly reduce the time to autism spectrum disorder (ASD) diagnoses from months to days, facilitating timely intervention and improving developmental outcomes.^97,98 Moreover, predictive models leveraging AI can stratify risk and guide clinical decisions for coexisting conditions often prevalent among people with disabilities, such as cardiovascular disease in patients with Down syndrome.⁹⁹ AI-driven decision support tools are increasingly integrated into clinical workflows, enhancing diagnostic consistency, minimizing human error, and improving therapeutic planning.^100,101 Moreover, human-centered AI frameworks emphasize transparency, interpretability, and inclusive interface design, ensuring that AI-driven diagnostics remain accessible to disabled users across sensory and cognitive disability groups. However, challenges remain, including algorithmic bias, insufficient data from underrepresented disability groups, and the necessity for explainable AI to ensure trust and clinical adoption.^102,103 Nonetheless, evidence suggests that AI has the capacity to mitigate disparities in healthcare access for people with disabilities, offering transformative tools for both clinicians and patients in the diagnostic and treatment journey.^104,105

Personalization of care

AI's potential to deliver personalized care solutions marks a paradigm shift toward disability-inclusive healthcare, aligning closely with the human-centric vision of Industry 5.0. Personalization, powered by AI, leverages patient-specific data—ranging from genomics to daily behavioral patterns—to tailor interventions precisely to individual needs and preferences.^106,107 For individuals with disabilities who frequently experience overlapping conditions and unique functional challenges, AI enables dynamic customization of treatment plans, therapies, and assistive technologies. For instance, AI-driven rehabilitation platforms, like XRHealth, adjust exercise difficulty and feedback in real-time, boosting patient engagement and optimizing therapeutic outcomes.^108,109 In the context of communication disabilities, AI-fueled augmentative and alternative communication (AAC) apps such as Avaz offer predictive text and adaptive interfaces, empowering non-verbal users to communicate effectively in their native languages and cultural contexts.^{71,80,110,111} AI-powered wearable devices continuously monitor physiological signals and environmental cues, allowing proactive adjustments in care, crucial for individuals with chronic disabilities prone to sudden health events.^81,112 Human-centered AI (HCAI) frameworks further emphasize transparency and usability, ensuring technologies remain accessible, ethical, and trustworthy.^82,113 However, achieving effective personalization demands inclusive co-design with people with disabilities, to avoid one-size-fits-all solutions and ensure cultural and contextual relevance.^114,115 As AI technologies continue evolving, their integration into personalized care holds the promise of enhancing autonomy, quality of life, and health equity for diverse disability communities, fundamentally reshaping disability-inclusive healthcare landscapes.^116,117

Case studies and success stories

The following section highlights real-world applications of Industry 5.0 principles, focusing on human-centeredness, personalization, and intelligent technology, to develop inclusive healthcare systems for people with disabilities. These cases demonstrate how emergent and AI technologies significantly enhance healthcare accessibility and quality for diverse groups with disabilities.

XRHealth: Virtual reality for remote disability rehabilitation

XRHealth, a US-based company, exemplifies the integration of VR and AI into remote rehabilitation services, particularly for individuals with cognitive, neurological, and physical disabilities. Originating in 2016 and approved by the US FDA, the platform provides immersive, interactive therapy experiences that are monitored in real-time by clinicians.¹¹⁸ The technology combines VR headsets with motion tracking and biometric sensors to create customized therapeutic environments.¹¹⁹ Patients undergo gamified exercises designed to enhance motor and cognitive functions. At the same time, clinicians track performance through AI-driven dashboards that adapt treatment plans based on real-time feedback, as shown in Figure 7.

Figure 7.

Workflow of XRHealth’s VR-based closed-loop rehabilitation system integrating AI and clinician feedback.

The implementation of XRHealth gained momentum during the COVID-19 pandemic, offering a safe and accessible alternative to in-clinic rehabilitation. It has proven especially valuable for patients with limited mobility or those living in remote areas. The platform demonstrated positive outcomes, including increased therapy engagement, better functional recovery, and reduced care costs. Lessons learned from XRHealth emphasize the importance of human–AI collaboration, simplicity in user interface design, and data privacy safeguards. Potential pitfalls include the need for continuous user training, sufficient internet infrastructure, and ensuring emotional engagement without a physical therapist's presence. Nevertheless, XRHealth stands as a scalable model for future disability-inclusive, remote healthcare delivery systems, aligning with Industry 5.0 values. These insights are further illustrated in Table 5, which compares traditional rehabilitation outcomes with those achieved using the XRHealth platform.

Table 5.

Comparison of traditional vs. XRHealth rehabilitation outcomes.

Aspect	Traditional Rehabilitation	XRHealth VR-Based Rehabilitation
Accessibility	Limited by patient mobility, location, or transport	Remote access from home; usable in rural/underserved areas
Therapist involvement	In-person sessions, time-limited	Real-time remote monitoring with flexible session timing
Personalization	Generalized exercise routines	AI-driven adaptive difficulty and patient-specific task progression
Patient engagement	Often low due to repetitive/manual tasks	High engagement through gamified, interactive environments
Monitoring and feedback	Therapist tracks progress manually during the session	Continuous biometric tracking; AI analytics deliver instant insights
Data-driven adjustments	Progress is measured via therapist observation	Real-time data triggers automatic adjustments to therapy levels
Cost and time efficiency	Requires facility use; travel time/costs for patients	Reduces travel, clinic wait time, and in-facility costs
During COVID-19	Major disruptions in therapy access	Enabled continuity of care through remote sessions
Outcomes reported	Improvement depends on patient attendance and therapist availability	Increased functional recovery, therapy adherence, and patient autonomy

Seha Virtual Hospital (Saudi Arabia): AI-enhanced telehealth for inclusive healthcare

Seha Virtual Hospital, launched in 2022 by Saudi Arabia's Ministry of Health, is recognized as the world's most extensive integrated telehealth system and a transformative model for disability-inclusive healthcare in the Middle East. The platform was developed to connect over 200 hospitals across the Kingdom, offering specialized healthcare services to patients, including those with disabilities, regardless of their geographic location.¹²⁰ The technology infrastructure underpinning Seha Virtual Hospital includes AI-powered diagnostics, digital twins for clinical simulations, remote patient monitoring systems, and real-time AR consultations. These technologies allow patients with physical, cognitive, or chronic conditions to receive personalized healthcare while remaining in their homes or local clinics. The AI components aid in triage, risk prediction, and decision support for clinicians, thereby enhancing diagnostic accuracy and reducing service bottlenecks.¹²¹ The implementation of Seha has been particularly impactful in expanding access for people in rural and underserved areas, many of whom face mobility limitations or transportation barriers. Figure 8 illustrates how Seha's telehealth services map to various disability types and contribute to inclusive healthcare outcomes. The platform facilitates virtual consultations, follow-up care, chronic disease management, and remote therapy, improving care continuity and reducing travel-related burdens for patients with disabilities. It also contributed to the resilience of the healthcare system during the COVID-19 pandemic by supporting remote triage and treatment. Key lessons from Seha underscore the importance of a strong governmental commitment, the integration of multi-speciality services, and user-centric design in telemedicine platforms. Challenges include ensuring digital literacy among patients, maintaining cybersecurity, and coordinating cross-institutional data sharing. Despite these challenges, Seha represents a scalable and sustainable model for leveraging emergent and AI technologies to build a more equitable and accessible healthcare system aligned with Industry 5.0 goals.

Figure 8.

Mapping Seha Virtual Hospital's telehealth services to specific disability types and inclusive outcomes.

Be My Eyes: AI-powered visual assistance for the visually impaired

Be My Eyes is a globally recognized mobile application that supports individuals with visual impairments by combining crowdsourced volunteer assistance and AI. Founded in Denmark in 2015, it began as a peer-to-peer service connecting blind or low-vision users with sighted volunteers via live video calls to assist with real-time visual tasks such as reading labels, navigating unfamiliar environments, or checking expiration dates.¹²² In 2023, the platform expanded its capabilities by integrating OpenAI's GPT-4 Vision model through the “Be My AI” feature. This enables users to take photos and receive detailed, AI-generated descriptions, read text, interpret documents, and ask follow-up questions—all without requiring human intervention. Figure 9 illustrates a timeline of key milestones in the app's development. This advancement marks one of the earliest real-world applications of multimodal AI for accessibility.¹²³ Be My Eyes has achieved a truly global impact, serving over half a million visually impaired users and supported by more than six million volunteers across 150 countries. Its hybrid model—blending AI with human assistance—ensures that users can access both fast automated support and personalized help when needed. Strategic partnerships with companies like Microsoft and Google have also enabled access to trained accessibility support directly within the app. The platform has significantly improved independence and confidence among users, reducing reliance on caregivers and enhancing day-to-day autonomy. The integration of AI has enabled more private and immediate assistance in sensitive contexts. By centering on empathy, inclusivity, and technological empowerment, Be My Eyes exemplifies the human-centric ethos of Industry 5.0 and demonstrates how AI can complement, rather than replace, human connection in delivering inclusive healthcare solutions.

Figure 9.

Timeline of key milestones and innovations in the development of Be My Eyes and its AI-powered expansion.

Avaz: Assistive communication for non-verbal disabilities

Avaz is a pioneering augmentative and alternative communication (AAC) app developed in India to support individuals with speech and communication impairments, particularly children with autism, cerebral palsy, Down syndrome, and other non-verbal disabilities. Launched in 2009 by Invention Labs, it stands out as one of the first major assistive tech innovations from the Global South, embodying Industry 5.0's human-centric and inclusive ideals.¹²⁴ The app enables users to communicate through picture symbols, text-to-speech synthesis, and customizable vocabulary boards. Designed with motor and cognitive challenges in mind, Avaz also incorporates AI-powered word prediction and is available in multiple regional languages, enhancing accessibility across linguistically diverse and underserved communities.¹²⁵ Avaz blends intelligent technology with practical, localized design. Features like dynamic vocabulary suggestions, offline support, and caregiver dashboards make it suitable for low-resource settings. The platform also includes training tools for parents and educators, promoting inclusive communication and learning. Lessons learned from Avaz's journey highlight the importance of cultural and linguistic adaptation in global assistive technology. While many AAC apps originated in the West, Avaz's localized development approach made it more relatable and practical in the Indian and Global South context. Another key insight is the value of ongoing feedback from end users, families, therapists, and educators, which helped refine the app into a scalable, user-friendly solution. Avaz demonstrates how intelligent assistive communication platforms can empower non-verbal individuals by restoring their voice, literally and metaphorically. By integrating AI into inclusive and culturally sensitive design, Avaz makes a meaningful contribution to the development of equitable, disability-friendly healthcare and educational systems that align with Industry 5.0 principles. The usage of the Avaz AAC App is summarized in Table 6.

Table 6.

Use cases of the Avaz AAC app across different disability types.

Disability type	Age group	Primary challenges	Avaz use case example	Outcome improvements
Autism spectrum disorder	Children (5–15)	Non-verbal expression, social cues	Expressing needs, emotions, and classroom answers	Increased interaction and reduced meltdowns
Cerebral palsy	Children and teens	Motor control, articulation issues	Tap-based requests, picture-based commands	Improved independence in daily routines
Down syndrome	Children	Speech clarity, vocabulary delays	Structured Q&A, guided vocabulary building	Enhanced language development and focus
Stroke survivors	Adults (40+)	Loss of speech post-incident	Re-learning words, simple requests via symbols	Faster recovery and improved confidence
Non-verbal learning delay	Teens and adults	Language comprehension, expression	Sequencing thoughts, contextual storytelling	Better classroom engagement and clarity
Traumatic brain injury	Adults (30–60)	Cognitive processing, memory	Daily task planning, communication scaffolding	More consistent rehabilitation progress

Cognoa's Canvas DX: AI-powered autism diagnosis for inclusive early healthcare

Canvas Dx, developed by the US-based health tech company Cognoa, is the first FDA-authorized AI diagnostic tool for early autism detection in children aged 18–72 months, Fig. 10. It integrates machine learning with inputs from caregiver questionnaires, home-recorded videos, and clinician assessments to generate accurate ASD diagnoses through a secure platform.¹²⁶ By processing this multimodal data with clinically validated algorithms, Canvas Dx significantly reduces diagnosis time from months to days, facilitating earlier access to therapy. Designed for use in primary care settings by non-specialists, the tool helps bridge diagnostic gaps in underserved communities.¹²⁷ Several lessons have emerged from the Canvas Dx journey. First, it underlines the feasibility of deploying AI-based diagnostic tools in early childhood, a particularly delicate and high-impact developmental window. Second, it demonstrates the value of human–AI collaboration, where the AI supports but does not replace the clinical judgment of trained professionals. Third, it emphasizes the need for robust and diverse training datasets to ensure the tool performs reliably across different demographic groups, thereby avoiding algorithmic bias. Lastly, it highlights that regulatory approval, particularly by agencies like the FDA, is key to building public and institutional trust in AI healthcare solutions. Canvas Dx aligns seamlessly with Industry 5.0's vision by promoting personalized, accessible, and ethically aligned healthcare systems. Through intelligent diagnostics that empower caregivers, streamline clinical workflows, and reach underserved populations, it serves as a blueprint for integrating AI into inclusive pediatric and neurodevelopmental care.

Figure 10.

Diagnostic process flow of Canvas Dx for early autism detection using AI, clinician input, and behavioral data.

Cross-case summary and lessons learned

To draw broader insights from the preceding case studies, Table 7 summarizes the key elements of each initiative, including the type of technology, implementation scale, focus on disability, and notable outcomes. These case studies collectively underscore the multifaceted role of emergent and AI-driven technologies in shaping inclusive healthcare. From real-time diagnostics and assistive communication to immersive therapy and hybrid visual support, the projects demonstrate the core principles of Industry 5.0—personalization, ethical AI integration, and human–machine collaboration. A recurring theme across successful implementations is the centrality of accessibility, whether achieved through cultural localization (Avaz), regulatory validation (Canvas Dx), or platform scalability (Be My Eyes). However, these initiatives also highlight challenges such as digital literacy, infrastructural readiness, and the ongoing need for human oversight in sensitive health interactions. Across the reviewed studies, it was concluded that technologies originally conceived within the automation logic of IR 4.0 are being reimagined through the human-centric, co-adaptive, and ethical principles of IR 5.0. Techniques such as telemedicine and wearables extend inclusion by enhancing remote accessibility and personalization, while VR and AR, as well as collaborative robots, embody the human-in-the-loop paradigm by placing empathy, comfort, and user agency at the center of technological interaction. As a consequence, these developments signal a transition from efficiency-driven digitalization to value-driven human empowerment.

Table 7.

Summary of AI-driven applications for disability-inclusive healthcare.

Attribute	XRHealth	Seha Virtual Hospital	Be My Eyes + Be My AI	Avaz AAC App	Cognoa Canvas Dx
Country	USA	Saudi Arabia	Denmark/Global	India/Global	USA
Primary focus	Remote VR-based rehabilitation for cognitive/physical disabilities	AI-powered telehealth for inclusive nationwide care	Visual support for blind users via human–AI hybrid	Augmentative communication for non-verbal individuals	Early autism diagnosis using AI and clinician input
Technology used	Virtual Reality, Motion Sensors, AI Analytics	AI Diagnostics, Telehealth Platform, Digital Twin	GPT-4 Vision AI, Video Chat, Specialized Help	Symbol-based AAC, Predictive AI, Multilingual UI	Machine Learning, Mobile App, Clinician Portal
Target disability	Neurological disorders, physical impairments	All disability types incl. mobility, chronic, and cognitive	Blindness and low vision	Autism, Cerebral Palsy, Down Syndrome	Autism Spectrum Disorder (ASD)
Implementation scale	USA-wide deployment, pandemic-driven expansion	National (Saudi Arabia), covering 200+ hospitals	Global with over 6 million users	Thousands of schools and therapy centers globally	Clinical adoption across pediatric networks in USA
Impact	Increased engagement, better rehab outcomes, remote access	Reduced access barriers, rural inclusion, pandemic resilience	Enhanced independence, real-time assistance, and growing AI use	Improved communication, language development, reduced caregiver stress	Accelerated diagnosis timelines, accessible in primary care
Lessons learned	Game-like features boost therapy compliance; infrastructure and training are vital	Government backing and service integration drive success	Human–AI hybrid maximizes usability; privacy is key	Localization and caregiver feedback improve adoption	Trust, clinician–AI synergy, and dataset diversity matter

While these reviewed technologies illustrate significant progress toward disability-inclusive healthcare, their tangible results demand more than technical advancement alone. In fact, effective implementation within the IR 5.0 framework requires comprehensive training and capacity-building programs for both disabled people and healthcare man-power. Equally, robust regulatory and ethical frameworks are critical factors that must evolve to govern data protection, algorithmic accountability, and patient consent in AI-driven systems. In parallel, ensuring data privacy and cybersecurity is paramount, as the sensitive nature of disability-related health data demands strict compliance with global data-protection standards. Moreover, the type of interaction between a large panel of disability types, different technical resources, and various domains of experts and trainers challenges the interdisciplinary collaborative participation, which is crucial to transform laboratory innovation into practical, inclusive, and sustainable solutions. Besides, the improvement of these emergent technologies for more successful real-world application without taking into account the feedback from people with disabilities at every design stage is considered a great challenge both for developers and users with disabilities. Thinking about all these elements needs the proposition of a roadmap with systemic transformation toward inclusive, resilient, and ethically aligned healthcare ecosystems.

Future directions

The future of disability-inclusive healthcare is being shaped by a new wave of technological innovations rooted in the human-centric values of Industry 5.0. A key trend is the rise of multimodal AI assistants that combine vision, speech, and contextual awareness to support users with complex disabilities, interpreting not just text and voice but also gestures and environmental cues. Another significant advancement is brain-computer interfaces (BCIs), which enable individuals with severe physical or speech impairments to control devices using neural signals, offering new possibilities for communication and mobility. Hyper-personalised rehabilitation is also emerging, using biometric sensors, emotional AI, and real-time analytics to adapt therapy protocols dynamically to each user's cognitive and emotional state. Additionally, augmented reality (AR) is being explored as a cognitive aid, helping users’ complete daily tasks independently through smart glasses or mobile devices. Collectively, these innovations mark a shift toward intelligent, personalized, and empowering healthcare systems for a more inclusive future.

Despite significant progress in AI and emergent technologies for disability-inclusive healthcare, several critical challenges persist. Accessibility and digital equity remain major concerns, especially in rural or low-resource settings lacking adequate infrastructure and connectivity. The high cost of advanced tools, such as brain–computer interfaces and VR systems, also limits their widespread adoption. Data privacy and the ethical use of AI pose further barriers, particularly when handling sensitive health information. A lack of inclusive design, often excluding direct input from people with disabilities, results in solutions that are technologically advanced but practically inaccessible. Lastly, limited training for both users and healthcare providers hinders effective implementation, underscoring the need for ongoing education and capacity building.

Finally, to operationalize the transition from conceptual innovation to practical application, a structured roadmap is proposed, encompassing six key pillars, Figure 11: (i) training and capacity building, (ii) regulatory and ethical governance, (iii) interdisciplinary collaboration, (iv) socio-economic enablement, (v) real-world validation, and (vi) inclusive co-design. These interconnected domains collectively define the enabling environment for the sustainable deployment of IR 5.0 technologies in disability-inclusive healthcare. Their integration ensures that technological advances are matched with human readiness, ethical assurance, and social equity, paving the way toward resilient, human-centered digital health ecosystems. Table 8 illustrates the structural overview of this proposed roadmap framework for implementation.

Figure 11.

Pillars of IR 5.0 roadmap for disability-inclusive healthcare.

Table 8.

Structural overview of the proposed roadmap framework.

Six pillar	Core focus	Strategic actions	Expected outcomes
1. Training and capacity building	Empower healthcare providers, technologists, and patients to effectively use emergent tools	- Develop multidisciplinary training modules on AI ethics, telemedicine interfaces, and assistive robotics. - Integrate digital literacy programs for patients with disabilities. - Establish academic–clinical–industry training partnerships	Improved technology adoption, reduced user error, enhanced trust and confidence
2. Regulatory and ethical governance	Establish clear, adaptive frameworks ensuring transparency and safety	- Align national policies with WHO and OECD AI ethics guidelines. - Define certification standards for medical AI and robotic devices. - Mandate ethical audits and bias mitigation in datasets.	Safe, accountable, and compliant deployment of Industry 5.0 technologies
3. Interdisciplinary collaboration	Foster co-creation among healthcare experts, engineers, policymakers, and disability advocates	- Establish cross-sector innovation hubs - Encourage participatory design workshops, including persons with disabilities - Promote open data and interoperability standards	Human-in-the-loop innovation; inclusive, sustainable design practices
4. Socioeconomic enablement	Address affordability, infrastructure, and access disparities	- Implement cost–benefit analyses to guide public investment - Develop low-cost prototypes for LMIC contexts - Integrate cloud-based solutions for remote regions	Scalable and equitable diffusion of technologies across diverse populations
5. Real-world evidence and validation	Demonstrate tangible impact through longitudinal, field-based research	- Conduct pilot programs in hospitals and rehabilitation centers - Establish real-world evidence registries - Measure clinical, social, and economic outcomes over time	Strong evidence base for policy adoption and investor confidence
6. Inclusive and participatory co-design	Embed lived experiences of people with disabilities in innovation cycles	- Apply universal design and accessibility standards - Involve disability representatives in all design and testing stages - Incorporate continuous user feedback loops	Technologies that are functional, accessible, culturally relevant, and trusted by users

Conclusion

This review has critically examined the intersection between emergent technologies, artificial intelligence, and disability-inclusive healthcare within the evolving framework of Industry 5.0. By emphasizing human-centricity, personalization, and the ethical integration of technology, the study underscores how Industry 5.0 offers a paradigm shift from automation-centered care to collaborative and empathetic systems. Through the analysis of current literature, global strategies, and pioneering case studies, it is evident that these technologies can transform healthcare experiences for people with disabilities by improving access, autonomy, and tailored interventions.

Moreover, the study identified promising trends such as brain–computer interfaces, multimodal AI assistants, and context-aware smart systems that will further enhance inclusivity and responsiveness in future healthcare models. However, several challenges remain, including digital infrastructure gaps, ethical concerns, affordability, and the need for inclusive co-design. To truly realize the potential of these technologies, stakeholders must prioritize accessibility, cross-sector collaboration, and user-centered innovation. Ultimately, this review contributes a forward-looking foundation for researchers, policymakers, and healthcare innovators aiming to design equitable, resilient, and inclusive healthcare systems that empower people with disabilities in the era of Industry 5.0.

To fully realize the potential of emergent and AI technologies in disability-inclusive healthcare, coordinated collaboration among governments, healthcare providers, and tech companies is essential. Policymakers must support inclusive innovation through regulation and infrastructure, while healthcare institutions adopt user-centered technologies. Tech developers should engage people with disabilities in co-design to ensure accessibility and cultural relevance. Such multi-stakeholder efforts are key to building equitable, resilient healthcare systems aligned with Industry 5.0's human-centric vision.

Footnotes

Acknowledgments

The authors extend their appreciation to the King Salman Center for Disability Research for funding this work through Research Group No. KSRG-2024-465.

ORCID iD

Mohamed Kchaou

Contributorship

Conceptualization, M.K., A.F.A.; methodology, M.K.; software, Y.M.; validation, M.K. and K.A.A.; formal analysis, K.A.A.; investigation, M.K., A.F.A.; resources, Y.M.; data curation, K.A.A.; writing—original draft preparation, M.K. and Y.M.; writing—review and editing, K.A.A.; visualization, M.K. and K.A.A.; supervision, M.K.; project administration, M.K. and K.A.A.; funding acquisition, K.A.A. and M.K. All authors have read and agreed to the published version of the manuscript.

Institutional review board statement

Not applicable.

Institutional review board statement

Not applicable.

Informed consent statement

Not applicable.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the King Salman Center for Disability Research (grant number KSRG-2024-465).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Oztemel

Gursev

. Literature review of Industry 4.0 and related technologies. J Intell Manuf 2020; 31: 127–182.

Golovianko

Terziyan

Branytskyi

, et al. Industry 4.0 vs. Industry 5.0: co-existence, transition, or a hybrid. Procedia Comput Sci 2023; 217: 102–113.

Longo

Nicoletti

Padovano

. Ubiquitous knowledge empowers the smart factory: the impacts of a service-oriented digital twin on enterprises’ performance. Annu Rev Control 2019; 47: 221–236.

Jamwal

Agrawal

Sharma

, et al. Industry 4.0 technologies for manufacturing sustainability: a systematic review and future research directions. Appl Sci 2021; 11: 5725.

Demir

Döven

Sezen

. Industry 5.0 and human-robot co-working. Procedia Comput Sci 2019; 158: 688–695.

Hanif

Iftikhar

. Post COVID-19 industrial revolution 5.0: the Dawn of cobot, chipbot and curbot. Pak J Surg Med 2020; 1: 122–126.

Kadir

Broberg

. Human well-being and system performance in the transition to Industry 4.0. Int J Ind Ergon 2020; 76: 102936.

Stentoft

Wickstrøm

Philipsen

, et al. Drivers and barriers for industry 4.0 readiness and practice: empirical evidence from small and medium-sized manufacturers. Prod Plan Control 2021; 32: 811–828.

Jeffri

NFS

Rambli

DRA

. A review of augmented reality systems and their effects on mental workload and task performance. Heliyon 2021; 7: e06522.

10.

Leng

Sha

Wang

, et al. Industry 5.0: prospect and retrospect. J Manuf Syst 2022; 65: 279–295.

11.

Tanjung

Ghazali

Mahmood

, et al. Drivers and barriers to industrial revolution 5.0 readiness: a comprehensive review of key factors. Green Technol Sustain 2025; 3: 100217.

12.

Ghobakhloo

Iranmanesh

Morales

, et al. Actions and approaches for enabling Industry 5.0-driven sustainable industrial transformation: a strategy roadmap. Corp Soc Responsib Environ Manag 2023; 30: 1473–1494.

13.

European Commission. Industry 5.0: a transformative vision for Europe – governing systemic transformations towards a sustainable industry. Brussels: Publications Office of the European Union, 2021, doi:10.2777/17322.

14.

Arksey

O’Malley

. Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005; 8: 19–32.

15.

Hashemi

Wickenden

Bright

, et al. Barriers to accessing primary healthcare services for people with disabilities in low- and middle-income countries: a meta-synthesis of qualitative studies. Disabil Rehabil 2022; 44: 1207–1220.

16.

Machado

Winroth

Ribeiro da Silva

EHD

. Sustainable manufacturing in Industry 4.0: an emerging research agenda. Int J Prod Res 2020; 58: 1462–1484.

17.

Matin

Williamson

Karyani

, et al. Barriers in access to healthcare for women with disabilities: a systematic review in qualitative studies. BMC Womens Health 2021; 21: 44.

18.

Santos

. Card sort technique as a qualitative substitute for quantitative exploratory factor analysis. Corp Commun Int J 2006; 11: 288–302.

19.

Devendra

Makawa

Kazembe

, et al. HIV and childhood disability: a case-controlled study at a paediatric antiretroviral therapy centre in Lilongwe, Malawi. PLoS One 2013; 8: e84024.

20.

Bunning

Gona

Odera-Mung’ala

, et al. Survey of rehabilitation support for children 0–15 years in a rural part of Kenya. Disabil Rehabil 2014; 36: 1033–1041.

21.

Taimoor

Rehman

. Reliable and resilient AI and IoT-based personalised healthcare services: a survey. IEEE Access 2022; 10: 535–563.

22.

Martini

Bellisario

Coletti

. Human-centered and sustainable artificial intelligence in industry 5.0: challenges and perspectives. Sustainability 2024; 16: 5448.

23.

Altiparmakogullari

Cifter

. A multidisciplinary inspection of dental photography: what do dentists think and what can designers do? Des J 2017; 20: S1989–S1997.

24.

Fischer

Safaeinili

Haverfield

, et al. Approach to human-centered, evidence-driven adaptive design (AHEAD) for health care interventions: a proposed framework. J Gen Intern Med 2021; 36: 1041–1048.

25.

Crnokić

Peko

Gotlih

. The development of assistive robotics: a comprehensive analysis integrating machine learning, robotic vision, and collaborative human assistive robots. In: Proc Int Conf Digit Transform Educ AI Appl. Cham: Springer; 2024. p.164–214.

26.

Gualtieri

Fraboni

Brendel

, et al. Updating design guidelines for cognitive ergonomics in human-centred collaborative robotics applications: an expert survey. Appl Ergon 2024; 117: 104246.

27.

Khalid

Naeem

Stasolla

, et al. Impact of AI-powered solutions in rehabilitation process: recent improvements and future trends. Int J Gen Med 2024; 17: 943–969.

28.

Bond

Mulvenna

Potts

, et al. Digital transformation of mental health services. npj Ment Health Res 2023; 2: 13.

29.

Shah

SIH

Naeem

Paragliola

, et al. An AI-empowered infrastructure for risk prevention during medical examination. Expert Syst Appl 2023; 225: 120048.

30.

Matin

Williamson

Karyani

, et al. Barriers in access to healthcare for women with disabilities: a systematic review in qualitative studies. BMC Womens Health 2021; 21: 44.

31.

Nahavandi

Alizadehsani

Khosravi

, et al. Application of artificial intelligence in wearable devices: opportunities and challenges. Comput Methods Programs Biomed 2022; 213: 106541.

32.

Bharath

MRR

. Controlling mouse and virtual keyboard using eye-tracking by computer vision. J Algebraic Stat 2022; 13: 3354–3368.

33.

Yang

Jin

Zhang

, et al. Mixed-modality speech recognition and interaction using a wearable artificial throat. Nat Mach Intell 2023; 5: 169–180.

34.

Almufareh

Kausar

Humayun

, et al. A conceptual model for inclusive technology: advancing disability inclusion through artificial intelligence. J Disabil Res 2024; 3: 20230060.

35.

Greshake Tzovaras

Angrist

Arvai

, et al. Open humans: a platform for participant-centered research and personal data exploration. GigaScience 2019; 8: giz076.

36.

Ginis

KAM

van der Ploeg

Foster

, et al. Participation of people living with disabilities in physical activity: a global perspective. Lancet 2021; 398: 443–455.

37.

Ogundele

Morton

. Classification, prevalence and integrated care for neurodevelopmental and child mental health disorders: a brief overview for paediatricians. World J Clin Pediatr 2022; 11: 120.

38.

Hou

. Ophthalmic health care needs of children with disabilities. Eye 2024; 38: 481–487.

39.

Hwang

Kim

, et al. Widening disparities in the national prevalence of diabetes mellitus for people with disabilities in South Korea. Public Health 2024; 226: 173–181.

40.

Elia

Monga

. Increased nephrolithiasis prevalence in people with disabilities: a national health and nutrition survey analysis. Urology 2022; 163: 185–189.

41.

Babalola

Sanguedolce

Dipper

, et al. Barriers and facilitators of healthcare access for autistic children in the UK: a systematic review. Rev J Autism Dev Disord 2024: 1–29. (in press).

42.

Star

Flores

Leyva

, et al. Barriers to routine dental care for children with special health care needs. Spec Care Dentist 2024; 44: 592–599.

43.

Net

Feliciano

Podsiadlo

, et al. Optimizing the patient experience for women with disabilities in the breast imaging clinic. J Breast Imaging 2024; 6: 183–191.

44.

Mitra

Long-Bellil

Moura

, et al. Advancing health equity and reducing health disparities for people with disabilities in the United States. Health Aff 2022; 41: 1379–1386.

45.

Ona

Strøm

Lee

, et al. Health inequalities and income for people with spinal cord injury: a comparison between and within countries. SSM Popul Health 2021; 15: 100854.

46.

Wang

Manning

Bogart

, et al. Predicting depression and anxiety among adults with disabilities during the COVID-19 pandemic. Rehabil Psychol 2022; 67: 179–189.

47.

Kaptein

Jansen

DEMC

Vogels

AGC

, et al. Mental health problems in children with intellectual disability: use of the strengths and difficulties questionnaire. J Intellect Disabil Res 2008; 52: 125–131.

48.

Moscoso-Porras

Fuhs

Carbone

. Access barriers to medical facilities for people with physical disabilities: the case of Peru. Cad Saude Publica 2019; 35: e00050417.

49.

Rhoden

Bonilha

Harvey

. Patient satisfaction of telemedicine remote patient monitoring: a systematic review. Telemed E Health 2022; 28: 1332–1341.

50.

Shorna

Sultana

Hasan

MAR

. Big data applications in remote patient monitoring and telemedicine services: a review of techniques and tools. SSRN Electron J 2024. doi:10.2139/ssrn.5026572

51.

Jiang

Zhi

, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2: 230–243.

52.

George

Cross

. Remote monitoring and telemedicine in IBD: are we there yet? Curr Gastroenterol Rep 2020; 22: 12.

53.

Atilgan

Onuk

Köksal Coşkun

, et al. Remote patient monitoring after cardiac surgery: the utility of a novel telemedicine system. J Card Surg 2021; 36: 4226–4234.

54.

Kodama

Arora

Anand

, et al. Reengineering the discharge transition process of COVID-19 patients using telemedicine, remote patient monitoring, and around-the-clock remote patient monitoring from the emergency department and inpatient units. Telemed E Health 2021; 27: 1188–1193.

55.

Pronovost

Cole

Hughes

. Remote patient monitoring during COVID-19: an unexpected patient safety benefit. JAMA 2022; 327: 1125–1126.

56.

Lukas

, et al. Emerging telemedicine tools for remote COVID-19 diagnosis, monitoring, and management. ACS Nano 2020; 14: 16180–16193.

57.

Mohammadzadeh

Rezayi

Saeedi

. Telemedicine for patient management in remote areas and underserved populations. Disaster Med Public Health Prep 2023; 17: e167.

58.

Sharma

Kakkar

. 3D-printed Personalized drug delivery systems: towards healthcare 5.0. Mater Today Chem 2023; 28: 101360.

59.

Haleem

Javaid

Singh

. 3D Printing applications in medical field: Industry 4.0 and 5.0 perspectives. Alex Eng J 2022; 61: 1239–1254.

60.

Meskó

Drobni

Bényei

, et al. Digital health is a cultural transformation of traditional healthcare. mHealth 2022; 8: 9.

61.

Bashir

Kim

. Telemedicine and AI integration for inclusive healthcare in the post-pandemic era. Healthcare (Basel) 2023; 11: 01.

62.

Sheikh

Anderson

Albala

. Digital health transformation and the global North–South divide. Lancet Digit Health 2023; 5: e12–e23.

63.

Mohapatra

Singh

. Human-centric AI in healthcare: ethical and regulatory perspectives. Front Digit Health 2023; 5: 1178123.

64.

Greiwe

Nyenhuis

. Wearable technology and how this can be implemented into clinical practice. Curr Allergy Asthma Rep 2020; 20: 36.

65.

Lamkin

. Smart wearables market to double by 2022: $27 billion industry forecast. Forbes. 2018. Available from: https://www.forbes.com/sites/paullamkin/2018/10/23/smart-wearables-market-to-double-by-2022-27-billion-industry-forecast/.

66.

Liang

Liu

Wang

. Algorithmic bias in healthcare AI: a systematic review. npj Digit Med 2023; 6: 22.

67.

Rajkomar

Dean

Kohane

. Machine learning in medicine. N Engl J Med 2019; 380: 1347–1358.

68.

Obermeyer

Mullainathan

. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019; 366: 447–453.

69.

Topol

. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25: 44–56.

70.

Kumar

Saini

. Wearable technology for disability assistance under Industry 5.0 framework. IEEE Access 2023; 11: 52131–52145.

71.

Chen

. Virtual-reality-based motor rehabilitation for limb disability: human-in-the-loop design. J Neuroeng Rehabil 2022; 19: 84.

72.

European Commission. Industry 5.0: towards a sustainable, human-centric and resilient European industry. Brussels: Directorate-General for Research and Innovation, 2021, doi:10.2777/17322.

73.

Nahavandi

. Industry 5.0—a human-centric solution. Sustainability 2022; 14: 1125.

74.

Vogel-Heuser

, et al. Industry 4.0 and Industry 5.0—inception, conception and perception. J Manuf Syst 2021; 61: 530–535.

75.

Oztemel

Gursev

. Transition to industry 5.0: human-centric perspective. Procedia Comput Sci 2020; 158: 688–695.

76.

Longo

Padovano

Umbrello

. Value-sensitive design of human–machine collaboration in Industry 5.0. Comput Ind 2021; 127: 103387.

77.

Javaid

Haleem

. Industry 5.0 applications in healthcare: a new challenge. J Ind Integr Manag 2022; 7: 1–16.

78.

Nguyen

Nakashima

. Assistive robotics under Industry 5.0 paradigm: opportunities and ethical challenges. Robotics 2023; 12: 47.

79.

Loureiro

Guerreiro

Rodrigues

. Collaborative robots in rehabilitation: review and future directions. Sensors 2023; 23: 4351.

80.

Dilsizian

Siegel

. Artificial intelligence in medicine and the real-world evidence gap. J Am Coll Radiol 2020; 17: 217–221.

81.

Umbrello

van de Poel

. Mapping value-sensitive design onto AI ethics. AI Soc 2023; 38: 1193–1209.

82.

García

Petri

. Ethics and policy of AI in healthcare 5.0: towards inclusive governance. Health Policy Technol 2024; 13: 100785.

83.

Alharthi

Kchaou

Munusamy

. Integrating AI and VR for disability-inclusive healthcare 5.0: Saudi case perspective. Diagnostics (Basel) 2024; 14: 51.

84.

Creed

Al-Kalbani

Theil

, et al. Inclusive AR/VR: accessibility barriers for immersive technologies. Univ Access Inf Soc 2024; 23: 59–73.

85.

Tsarouchi

Makris

Chryssolouris

. Human–robot interaction review and challenges on task planning and programming. Int J Comput Integr Manuf 2019; 32: 449–465.

86.

Villani

Pini

Leali

, et al. Survey on human–robot collaboration in industrial and service settings: safety, intuition, and ergonomics. Mechatronics (Oxf) 2018; 55: 248–266.

87.

Kim

Park

Lee

. Adaptive impedance control for safe human–robot physical interaction. IEEE Trans Ind Electron 2021; 68: 6012–6023.

88.

Nef

Guidali

Riener

. Rehabilitation robotics: supporting motor recovery after stroke and spinal cord injury. Eur J Phys Rehabil Med 2020; 56: 787–797.

89.

Tung

. Assistive robotic arms for activities of daily living: a systematic review. J Neuroeng Rehabil 2022; 19: 27.

90.

Kim

Cho

Lee

. Multimodal human–robot interface using computer vision and speech recognition for assistive robots. Sensors (Basel) 2023; 23: 4319.

91.

Martínez-González

Alenyà

Torras

. Safe and adaptive assistance through vision-based learning in domestic robotics. Robot Auton Syst 2022; 149: 103958.

92.

Romano

Spada

Caputo

. Collaborative robots for healthcare logistics and caregiver support: ergonomic and ethical implications. Front Robot AI 2024; 11: 1387125.

93.

Filipovic

Milosevic

. Augmented reality balance physiotherapy in HOLOBALANCE projectComputational modeling and simulation examples in bioengineering. Cambridge, MA, USA: Academic Press, 2021, pp.259–303.

94.

Smith

Chen

Patel

. AI In neuroimaging diagnostics for neurodevelopmental disorders. J Digit Health 2023; 5: 145–158.

95.

Zhou

Ling

Alhazmi

. Deep learning in disability-focused radiology. Radiol Artif Intell 2022; 4: 22–36.

96.

Park

Kim

Lee

. Machine vision for early diagnosis in cerebral palsy. Pediatr Neurol 2024; 148: 23–31.

97.

Bharath

Yang

Chu

. AI In ophthalmology for visual impairments. Ophthalmol Inform 2022; 38: 201–214.

98.

Megerian

Dey

Melmed

, et al. Performance of Canvas Dx, a novel software-based autism spectrum disorder diagnosis aid for use in a primary care setting. Neurology 2022; 98: 1025.

99.

Charalambous

Efstratopoulou

. Artificial intelligence approaches to autism spectrum disorder screening and diagnosis. In: AI in mental health: innovations, challenges, and collaborative pathways. Hershey, PA: IGI Global, 2025, pp.41–70. doi:10.4018/978-1-7998-9540-8.ch003.

100.

Lee

Ryu

Choi

. Predictive AI for cardiac risk in down syndrome. Cardiogenet J 2024; 12: 55–63.

101.

Rani

Chauhan

. Clinical decision support systems for disability care. Health Inform J 2023; 29: 88–97.

102.

Nguyen

Hossain

Kumar

. AI Integration in clinical workflows for disability management. BMC Med 2024; 22: 33.

103.

Obermeyer

Powers

Vogeli

, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019; 366: 447–453.

104.

Ghassemi

Oakden-Rayner

Beam

. The false hope of current explainable AI methods in healthcare. Nat Med 2021; 27: 676–680.

105.

Renda

Perez

. AI Applications for equitable disability care. Disabil Policy Stud J 2021; 32: 245–258.

106.

Renda

Misuraca

Simon

. Industry 5.0 and socio-environmental challenges. Eur Comm Policy Brief 2022. doi:10.2760/1234

107.

Topol

. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25: 44–56.

108.

Liu

Green

Ahmad

. AI for personalized healthcare pathways. Health Policy Technol 2023; 12: 102–113.

109.

Orr

Arbel

Levy

, et al. Virtual reality in the management of patients with low back and neck pain: a retrospective analysis of 82 people treated solely in the metaverse. Arch Physiother 2023; 13: 11.

110.

XRHealth. Our virtual clinics. XRHealth. 2022. Available from:.

111.

Sonawane

Varshneya

. AVAZ Application (trial version): a voice for the nonverbal children with autism spectrum disorder—a pilot study. Indian J Occup Ther 2020; 52: 5–12.

112.

Sreekumar

. Using AVAZ to enhance communicative abilities of a child with cerebral palsy. Disabil CBR Incl Dev 2020; 25: 95–102.

113.

Lukes

Petrovic

Bell

. Wearable health monitoring during COVID-19 pandemic. Sensors (Basel) 2021; 21: 3429.

114.

Ammanath

Smith

Zhao

. Human-centered AI: principles and practices. Deloitte Rev 2022; 31: 45–60.

115.

Pereira

Santos

Hossain

. Co-designing AI solutions for disability inclusion. Disabil Soc 2024; 39: 175–190.

116.

Hwang

Patel

Alavi

. Cultural adaptation in AI health apps for disability communities. Glob Health Action 2023; 16: 2198424.

117.

Yang

Rahman

Joseph

. Context-aware AI in disability care. Digit Health 2024; 10: 2055207624123456.

118.

Hussain

Ali

Rahimi

. AI For personalized disability rehabilitation. Front Digit Health 2022; 4: 876543.

119.

Seha. Ministry of Health, Saudi Arabia. Seha Virtual Hospital—Vision 2030. Ministry of Health Saudi Arabia. 2022. Available from:.

120.

Eyes

. Introducing Be My AI: virtual volunteer powered by GPT-4. Be My Eyes Blog. March 2023. Available from:.

121.

Be My Eyes. The story about Be My Eyes. Be My Eyes Official Site. 2023. Available from:.

122.

Avaz Inc. The award-winning speech app for autism and other speech disabilities. Avaz App. 2023. Available from:.

123.

Megerian

Dey

Melmed

, et al. Performance of canvas dx, a novel software-based autism spectrum disorder diagnosis aid for use in a primary care setting. Neurology 2022; 98: 1025.

124.

Charalambous

Efstratopoulou

. Artificial intelligence approaches to autism spectrum disorder screening and diagnosis. In: AI in Mental Health: Innovations, Challenges, and Collaborative Pathways. IGI Global; 2025:41-70. doi:10.4018/978-1-7998-9540-8.ch003.

125.

Nguyen

Nakashima

Munusamy

. Integrating AI and VR for disability-inclusive healthcare: a Saudi case perspective. Diagnostics (Basel) 2024; 14: 51.

126.

García

Petri

. Ethics and policy of AI in healthcare 5.0: towards inclusive governance. Health Policy Technol 2024; 13: 100785.

127.

Umbrello

van de Poel

. Mapping value-sensitive design onto AI ethics. AI Soc 2023; 38: 1193–1209.

Industry 5.0 adaptation for disability-inclusive healthcare: A review of emergent and AI technologies for assistive digital health

Abstract

Keywords

Introduction

Materials and methods

Results and discussion

Importance of disability inclusion in healthcare

Disabilities and impact on healthcare

Emergent technologies and disability diagnostics

Telemedicine and remote patient monitoring

Wearable health technology

Virtual reality (VR) and augmented reality (AR)

Collaborative robots in rehabilitation and daily care

Role of artificial intelligence in disability diagnostics

AI in diagnostics and treatment

Personalization of care

Case studies and success stories

XRHealth: Virtual reality for remote disability rehabilitation

Seha Virtual Hospital (Saudi Arabia): AI-enhanced telehealth for inclusive healthcare

Be My Eyes: AI-powered visual assistance for the visually impaired

Avaz: Assistive communication for non-verbal disabilities

Cognoa's Canvas DX: AI-powered autism diagnosis for inclusive early healthcare

Cross-case summary and lessons learned

Future directions

Conclusion

Footnotes

Acknowledgments

ORCID iD

Contributorship

Institutional review board statement

Institutional review board statement

Informed consent statement

Funding

Declaration of conflicting interests

References

Be My Eyes: AI-powered visual assistance for the visually impaired