Cybersecurity for Analyzing Artificial Intelligence (AI)-Based Assistive Technology and Systems in Digital Health

Abstract

Assistive technology (AT) is increasingly utilized across various sectors, including digital healthcare and sports education. E-learning plays a vital role in enabling students with special needs, particularly those in remote areas, to access education. However, as the adoption of AI-based AT systems expands, the associated cybersecurity challenges also grow. This study aims to examine the impact of AI-driven assistive technologies on cybersecurity in digital healthcare applications, with a focus on the potential vulnerabilities these technologies present. Methods: The proposed model focuses on enhancing AI-based AT through the implementation of emerging technologies used for security, risk management strategies, and a robust assessment framework. With these improvements, the AI-based Internet of Things (IoT) plays major roles within the AT. This model addresses the identification and mitigation of cybersecurity risks in AI-based systems, specifically in the context of digital healthcare applications. Results: The findings indicate that the application of the AI-based risk and resilience assessment framework significantly improves the security of AT systems, specifically those supporting e-learning for blind users. The model demonstrated measurable improvements in the robustness of cybersecurity in digital health, particularly in reducing cyber risks for AT users involved in e-learning environments. Conclusions: The proposed model provides a comprehensive approach to securing AI-based AT in digital healthcare applications. By improving the resilience of assistive systems, it minimizes cybersecurity risks for users, specifically blind individuals, and enhances the effectiveness of e-learning in sports education.

1. Introduction

AT and systems encompass advanced tools, devices, software, systems, and platforms designed to support individuals with disabilities by enhancing their independence, improving their quality of life, and ensuring equal access to opportunities in various aspects of daily life, such as education, mobility, employment, and healthcare [1,2]. These technologies and systems integrate artificial intelligence of things (AIoT), AI algorithms, machine learning (ML) models, natural language processing (NLP), computer vision, and data analytics to provide adaptive, intelligent, and personalized support for individuals and AT developers, including blind people involved with e-learning. Consequently, these technologies can dynamically respond to user needs, learn from interactions, and continuously improve over time to enhance users’ overall quality of life [3,4,5].

In digital health, AI-based assistive technologies and systems have distinguished themselves by providing innovative solutions aimed at enhancing patient experience, delivering accurate diagnostics, enabling the early detection of health issues, personalizing treatment plans, optimizing workflows, improving medical outcomes, and ensuring equitable access to healthcare services for all patients [6,7,8,9]. These technologies include robots and AI chatbots, AI applications and devices, remote patient monitoring (e.g., wearable devices), AI-powered imaging tools, and other advanced systems [10,11]. In addition to their primary benefits for patients, individuals with disabilities, and the elderly—such as enhanced independence, improved accessibility, and proactive healthcare—these technologies also offer cost efficiency by reducing hospital admissions and optimizing resource allocation. Moreover, their scalability allows for assistive healthcare solutions to become more widely available across large populations [12].

Although many AI-driven tools and devices operate continuously to ensure the trust, safety, and functionality of healthcare ecosystems without interruptions, some healthcare systems may still face serious cybersecurity challenges, such as occasional downtime or limited interoperability [13]. When AI medical systems are integrated into clinical workflows, they may introduce vulnerabilities and increase the risk of data breaches due to the sensitive nature of health data, the lack of safeguards for interconnected IoT networks, the unreliable operation of interconnected systems, and the uncertain reliability of AI algorithms. These factors contribute to potential cybersecurity threats, including data privacy and confidentiality risks, adversarial attacks through malicious AI model inputs, tampering with 3D medical imagery, IoT device exploitation, network vulnerabilities, unsecured APIs, unauthorized access, bias in AI algorithms, ransomware, denial-of-service (DoS) attacks, and an expanded attack surface [14,15,16].

To eliminate cybersecurity threats and ensure equitable, safe, and efficient healthcare delivery, addressing these security concerns requires a proactive, multi-layered strategy that integrates advanced security technologies, regulatory compliance, and user education [17,18,19,20]. Therefore, a comprehensive set of cybersecurity solutions must be increasingly integrated into various healthcare sectors and systems to enhance medical services [21]. This research identifies AI-based security solutions and tools, including tested AI algorithms and protocols, that can support both current and future assistive technologies in digital healthcare. These advancements play a crucial role in strengthening cybersecurity measures within medical workflows.

Figure 1 focuses on AIoT in healthcare applications used with modern health systems, AT management systems, and communication infrastructure for blind users. It demonstrates how essential components—such as neuron sensors and AI-powered smart sticks—work together with enabling technologies like IoMT and quantum-based AT to address health concerns. These technologies enable real-world applications in healthcare and e-learning, showcasing AIoT’s transformative impact on accessibility, digital inclusion, and overall healthcare innovation.

AIoT [22] and Figure 1 support researchers in developing cybersecurity algorithms and protocols for analyzing the AI-based AT and systems used in digital healthcare. However, the concept of cybersecurity must be considered to address potential security challenges. These challenges include data privacy and protection to ensure the confidentiality of sensitive patient data collected by sensors and devices in medical AIoT systems. Additionally, network security is essential for safeguarding and encrypting communication channels between devices, as well as between edge and cloud layers. Robust authentication and authorization mechanisms must be implemented to prevent unauthorized access. AI model security is also crucial in mitigating adversarial attacks, malware, ransomware, and distributed denial-of-service (DDoS) attacks, ensuring that AI models remain reliable, secure, and accurate. Furthermore, regulatory compliance with established security standards, such as the general data protection regulation (GDPR) and the health insurance portability and accountability act (HIPAA), must be maintained. To effectively mitigate these cybersecurity challenges in AI-based healthcare applications, essential security components—including encryption, secure communication protocols, access control, and threat detection mechanisms—must be incorporated to safeguard patient data and ensure the reliability of healthcare applications and systems. Keeping all terms accurate in GDPR during the security management of AIoT and e-learning based on AT will reduce the risks and unnecessary vulnerabilities that provide hackers with opportunities to carry out cyber-attacks such as introducing ransomware.

The primary goal of the proposed project is to analyze AI-based assistive technologies and systems using the proposed model. This research aims to measure the cybersecurity vulnerability levels of AI-based assistive systems when modern industries and organizations adopt various assistive technologies. It has two main goals: promoting the fundamental science needed to understand and establish security principles for designing and architecting such systems, and applying these principles using specific examples.

The AI-based cybersecurity outcomes of the proposed work on AI-based assistive systems are expected to have the following points of utilization/impact:

• Providing theoretical and technical foundations for securing AI-based assistive systems that support AI-based AT for digital health.
• Providing practical AI-based security tools for developing the proposed model and applying the tested algorithms and protocols.

Extensions of existing software-based security protocols, which account for the dynamically changing response times and security levels in the critical, harsh environment of digital health, will enhance the efficiency of assistive systems.

The proposed work may expand the security boundaries of the current cybersecurity issues within assistive systems, thus widening the operational possibilities of important security scenarios involving AI-based assistive systems and communication devices for autonomous services. AI-based assistive technologies are integrated into various systems used by individuals with physical disabilities.

Regarding the use of AI-based assistive systems in digital health, selected technical innovations and results from the proposed work will be disseminated through publications in academic forums (journals and conferences), industry-wide events, tradeshows, trade magazines, specially designed project workshops, and contributions to relevant standardization bodies.

The rest of this paper outlines a scheme for providing secure AI-based AT and systems in digital healthcare, structured as follows: Section 2 presents a literature review of cybersecurity, AI-based healthcare, AI-based AT, and existing analyses of secure assistive systems with emerging technology. Although the literature review provides only the basic details of AIoT in healthcare, it supports our attempt to proactively minimize risks and cyber-attacks (ransomware). Section 3 introduces the proposed method and an appropriate block diagram of wearable assistive systems for education environments. This allows us to analyze the security issues, using e-learning based on AT as an example to illustrate cybersecurity issues for blind users. Section 4 shows the results of relevant experiments, conducted using risk approaches and calculations, along with the necessary risks and other pertinent parameters of AI-based AT. Section 5 discusses security issues digital healthcare using an AI-based AT and one example of e-learning users (people with blindness). This analysis considers the risks in digital health data and analyzes the cybersecurity required to improve AI-based AT. Section 6 highlights the latest challenges facing AI-based AT influenced by quantum technology when used in digital healthcare, aiming to enhance future security by reducing costs and providing maximum accuracy. Finally, Section 7 provides the conclusions derived from the proposed results and an analysis. In our discussion of future work, we provide possible extensions involving the development of AI-based AT to provide maximum security in all AT applications.

2. Literature Review

Artificial intelligence (AI) has revolutionized the healthcare sector, particularly strengthening cybersecurity. As AI becomes more deeply integrated into healthcare systems, its role in protecting sensitive data has grown substantially. AI-powered systems are now capable of detecting and neutralizing threats in real time, offering robust defense against increasingly sophisticated cyberattacks.

2.1. AI and Cybersecurity in Healthcare Systems

In the rapidly developing sector of digital health, where new technologies like AI present both benefits and hazards, cybersecurity is crucial. Adopting AI-driven security solutions is therefore crucial [23,24].

Security, privacy, trust management, and other forms of data protection are crucial components enabled by communication networks in healthcare services [25,26,27,28].

Reference [24] explores the critical role of cybersecurity in AI-based healthcare communication (AHC), highlighting vulnerabilities and proposing solutions to safeguard sensitive data. It emphasizes the integration of robust AI-based intelligent cybersecurity measures with cybersecurity policies related to digital healthcare communication to ensure the reliability and privacy of digital healthcare systems, as shown in Figure 2. This model enables secure communication when AI-based assistive technology (AT) is employed in assistive systems and applications, such as e-learning.

By using the multi-source transfer learning method, Chakraborty et al. [29] presented a cyberattack detection system. AI-based systems are able to learn and adjust to new threats, providing dynamic and adaptable solutions to new cybersecurity concerns in the healthcare industry.

Using public datasets and news articles, Bertl [30] demonstrates how text mining and news analyses can be employed to detect cybercrime activities in digital healthcare, providing an additional layer of security against potential risks.

Jalali et al. [31] provide a comprehensive overview of the growing role of technological aspects in cybersecurity for protecting healthcare systems, employing bibliometric analysis to identify key trends and gaps in areas such as organizational and human factors, strategy, and management. Additionally, the cybersecurity considerations in medical imaging are discussed, with a focus on enhancing security, detection, and prevention strategies in radiology AI initiatives. These efforts aim to protect patient data and ensure the integrity of AI-driven diagnostic systems [32].

In addition to discussing the creation of self-optimizing and self-adaptive AI systems for healthcare cybersecurity, Radanliev and De Roure [33] propose the use of autonomous AI for predictive cyber risk analytics and anticipating medical production and supply chain bottlenecks during pandemics. The overall security architecture is strengthened by these systems’ ability to learn from previous events, adapt to new types of cyberattacks, and continuously improve.

Risks like data extraction, social engineering, and dataset poisoning are highlighted in Reference [14], which focuses on cybersecurity in medical equipment, especially the role of AI. It also provides an overview of the EU’s regulatory framework for ensuring AI cybersecurity in medical devices.

Telehealth, digital data integration, AI, cybersecurity, and qualitative hypoglycemia alerts are among the emerging technologies that could help reduce the burden of diabetes. Notably, cybersecurity considerations are essential to ensuring the safe and effective use of these technologies [34].

Reference [35] highlights the importance of establishing a strong legal framework to prevent fraud in initiatives such as Ayushman Bharat in India by ensuring the privacy and security of patient data within AI-powered healthcare systems. It discusses government regulations, legal measures, and cyber threats, which are especially significant in developing markets with a less advanced healthcare infrastructure. Conversely, Reference [36] advocates for the creation of an international task force to enhance financial coordination, infrastructure growth, and equitable access, aiming to advance digital healthcare and AI in global eye care.

Furthermore, Reference [37] suggests adopting a robust cybersecurity framework, performing periodic risk assessments, strengthening access controls, employing data encryption, training employees, and developing incident response strategies to reduce these risks.

2.2. Cybersecurity in Emerging Healthcare Technologies

The healthcare industry has undergone a revolution due to emerging technologies like Industry 4.0, 5G networks, the internet of things (IoT), and blockchain. These technologies present previously unheard-of opportunities to improve efficiency and innovation, but they also bring with them a number of cybersecurity issues that need to be resolved in order to protect healthcare systems.

IoT ecosystems are particularly vulnerable to assaults due to their complexity and large number of connected devices. By offering predictive danger detection and real-time traffic monitoring, AI and machine learning (ML) are essential in tackling these issues.

The cybersecurity infrastructure required to shield healthcare systems from the vulnerabilities present in IoT-enabled devices is examined by Vaghela and Shah [38]. Their study highlights how crucial it is to create robust cybersecurity frameworks that can survive the expanding threat landscape linked to IoT in the healthcare industry.

Messinis et al. [39] conducted a thorough study on how AI technologies, such as machine learning (ML) and deep learning (DL), enhance the cybersecurity of IoMT devices. The article highlights several security and privacy issues related to IoMT, including increased vulnerability to cyber-attacks resulting from growing interconnectivity. Furthermore, it emphasizes the importance of AI in improving the reliability and effectiveness of cybersecurity measures, and outlines future research goals focused on patient data protection and data-driven healthcare within the IoMT framework.

Vaisakhkrishnan et al. [40] discuss the various cyber threats faced by the internet of medical things (IoMT) and examine deep learning models capable of analyzing complex patterns and detecting anomalies. These models provide a robust intrusion detection system, ensuring reliable healthcare services while safeguarding sensitive medical data. To enhance trust and decision-making in e-healthcare settings, risk assessment techniques and a novel paradigm that emphasizes quantified risk assessment in IoMT systems with policies are investigated [41,42].

In their thorough analysis of the security vulnerabilities in multi-access edge computing (MEC) in 5G use scenarios, Ranaweera et al. [43] draw attention to the possibility of cyberattacks that could interfere with vital healthcare services. Their research emphasizes the necessity of strong security measures to guard against new dangers to the healthcare applications provided by 5G.

Using the nominal group technique (NGT), Musbahi et al. [44] highlight the potential of AI to improve management, speed up health services, and ensure equity in healthcare decision-making. However, they also highlight problems such as bias, decreased human engagement, algorithm errors, and technological constraints. The article reveals the need for strong cybersecurity measures, demonstrates a widespread concern about the security and privacy of personal data in AI-driven systems, and advocates for additional legislation and public involvement in patient care.

Additionally, the integration of AI in wearable technology and IoT in healthcare brings new challenges to cybersecurity, necessitating sophisticated approaches to safeguard interconnected devices and sensitive information. Ramasamy et al. [45] present an AI-powered IoT-CPS system aimed at aiding doctors in diagnosing conditions such as diabetes, heart disease, and abnormal gait patterns. Utilizing a Kaggle dataset, this system outperforms existing techniques in F-measure, accuracy, precision, and recall.

Conversely, blockchain and AI provide a potent remedy for the escalating cybersecurity issues that the healthcare sector and other sectors are confronting. AI improves real-time threat detection and response capabilities, while blockchain’s decentralized structure guarantees data integrity and immutability. These technologies work together to form a multi-layered security system that is extremely breach-resistant.

Ameen et al. [46] provide a comprehensive analysis of how integrating cybersecurity, blockchain, and AI technologies can address security challenges in healthcare applications, particularly within IoMT. They highlight the strengths of decentralized systems and the secure management of sensitive healthcare data across connected devices through the combination of AI and blockchain.

For cybersecurity in healthcare networks, Miriam et al. [47] propose a lionized golden eagle-based homomorphic elapid security (LGE-HES) algorithm that effectively and accurately detects fraudulent transmissions while ensuring the security of medical images.

Moreover, by merging and aggregating layers of learnt edge cloud models, the merged hierarchical deep learning system with layer reuse for security (MUSE) is proposed to increase the accuracy and speed of training enormous core cloud models [48] in cloud-based healthcare systems.

To reduce security flaws and unauthorized access in stand-alone solutions, a novel architecture for combining AI, IoT, and blockchain technologies is put forth for safer and smarter e-health data applications [49].

2.3. Cybersecurity for Analyzing AI-Based AT via Digital Healthcare Applications

In digital healthcare applications, medical and administrative issues are considered, but handling data containing risks creates cyber risks when diagnosing eye diseases. In this analysis, many symptoms, such as diabetes and cardiac problems, are proactively collected for risk predictions. Through using risky and risk-free data when diagnosing symptoms, cybersecurity solutions can be improved. This improvement supports blind people in the use of e-learning through the implementation of risk-free AI-based AT and systems. Table 1 allows for developers to analyze future AI-based AT systems to improve the security of digital healthcare.

Alshehri [50] offers a lightweight solution for secure data management in the healthcare area with a blockchain-integrated cybersecurity approach based on AI (BICS-AI). This approach leverages AI algorithms to quickly identify and respond to potential threats, while blockchain ensures the integrity of the data.

Table 1. Security issues in digital healthcare (R = ransomware; NR = non-ransomware).

Refs.	Scope	Remarks		Details of Cyber Issues
		Contributions	Limitations	Summary	Malware
[51]	Cyber risk insurance for ransomware	Proposes a mixed-method approach for evaluating and reducing risks using cyber risk insurance techniques. Emphasizes the need for proactive risk management.	Limited focus on non-ransomware cyber threats. Lacks a mixed-method procedure and increases the complexity of the space.	Discusses ransomware risks, their financial impact, cyber-insurance modeling, and risk management and mitigation strategies.	R
[52]	AI in ophthalmology	Reviews AI-based assistive applications in ophthalmology by focusing on the accuracy of diagnosis and treatment planning.	Lacks secure image management, and does not address data privacy, ethical considerations, or regulatory challenges when using AI in healthcare.	Raises concerns about data security related to the sensitive medical records used by AI systems. Lacks a thorough investigation of different healthcare-specific cyber threats.	R
[53]	Mixed reality (MR) and IoT for elderly assistive technology	Integrates mixed reality and IoT for elderly care in smart home environments, enhancing independence, safety, and quality of life.	Technical challenges related to user adoption and system interoperability in MR and IoT integration are not fully addressed.	IoT security concerns, including potential data breaches and unauthorized access.	NR
[54]	Assistive robots for autistic children	Explores how robots enhance cognitive abilities and foster learning experiences for children with autism spectrum disorder (ASD), providing evidence from experimental studies.	Widespread adoption might be hindered by high costs and technical challenges in robot deployment.	Limited discussion on cybersecurity risks in robotic systems, despite the reliance on connected devices, which could introduce vulnerabilities such as hacking or the misuse of personal data.	NR
[55]	Instructional strategies in assistive technology for learning disabilities	Conducts a meta-synthesis review of effective instructional strategies for assistive technology in learning disabilities, providing insights into best practices and pedagogical approaches.	Strategic AT must be reviewed; lacks empirical studies on long-term effectiveness.	No significant cybersecurity focus, although it highlights the need for secure digital platforms to protect student data.	NR
[56]	AI in e-learning systems	Explores AI technologies and applications in e-learning systems, including personalized learning, adaptive assessments, the automation of administrative tasks, and enhanced learner engagement.	Limited practical applications.	Discusses potential data privacy risks and issues related to security and data protection in AI-driven e-learning platforms.	NR
[57]	AIoT in assistive technology	A systematic review of AIoT applications in assistive technology, emphasizing innovations in healthcare, mobility aids, and smart living solutions.	No emphasis on challenges related to scalability and integration with existing systems.	Potential cyber issues include AIoT security vulnerabilities, data breaches, unauthorized access, and concerns regarding data integrity.	NR
[58]	AI assistive technology in hospital nursing	Designs a 3D human pose estimation system for home care robots, and how AI enhances professional nursing practices, including patient monitoring, diagnostics, workflow optimization, and decision support.	Lacks real-world case studies.	Security concerns related to patient data and medical data privacy.	R + NR
[59]	AI in prosthetic and orthotic rehabilitation	Reviews AI’s integration in rehabilitation technologies, including personalized design and functionality enhancements.	Limited cybersecurity perspective	No major cybersecurity risks are discussed, although connected prosthetic devices may be vulnerable to hacking or malfunction due to software flaws.	R + NR
[60]	AI in sports training	Explored AI applications in sports training through case studies, including optimizing athlete performance analysis, injury prevention, and training customization.	Lacks discussion of the practical implementation of AI in areas other than sports training.	Limited focus on significant cybersecurity issues, although wearable tech and AI systems used in sports could face threats such as data theft or system tampering.	NR
[61]	Emerging technologies in assistive tech	Reviews emerging technologies and their potential for creating new assistive technologies for people with disabilities.	General overview, lacks depth in AT applications and detailed implementation strategies.	Does not specify the types of cyber risks or mitigation strategies associated with emerging technologies.	NR

Table 1. Security issues in digital healthcare (R = ransomware; NR = non-ransomware).

Refs.	Scope	Remarks		Details of Cyber Issues
		Contributions	Limitations	Summary	Malware
[51]	Cyber risk insurance for ransomware	Proposes a mixed-method approach for evaluating and reducing risks using cyber risk insurance techniques. Emphasizes the need for proactive risk management.	Limited focus on non-ransomware cyber threats. Lacks a mixed-method procedure and increases the complexity of the space.	Discusses ransomware risks, their financial impact, cyber-insurance modeling, and risk management and mitigation strategies.	R
[52]	AI in ophthalmology	Reviews AI-based assistive applications in ophthalmology by focusing on the accuracy of diagnosis and treatment planning.	Lacks secure image management, and does not address data privacy, ethical considerations, or regulatory challenges when using AI in healthcare.	Raises concerns about data security related to the sensitive medical records used by AI systems. Lacks a thorough investigation of different healthcare-specific cyber threats.	R
[53]	Mixed reality (MR) and IoT for elderly assistive technology	Integrates mixed reality and IoT for elderly care in smart home environments, enhancing independence, safety, and quality of life.	Technical challenges related to user adoption and system interoperability in MR and IoT integration are not fully addressed.	IoT security concerns, including potential data breaches and unauthorized access.	NR
[54]	Assistive robots for autistic children	Explores how robots enhance cognitive abilities and foster learning experiences for children with autism spectrum disorder (ASD), providing evidence from experimental studies.	Widespread adoption might be hindered by high costs and technical challenges in robot deployment.	Limited discussion on cybersecurity risks in robotic systems, despite the reliance on connected devices, which could introduce vulnerabilities such as hacking or the misuse of personal data.	NR
[55]	Instructional strategies in assistive technology for learning disabilities	Conducts a meta-synthesis review of effective instructional strategies for assistive technology in learning disabilities, providing insights into best practices and pedagogical approaches.	Strategic AT must be reviewed; lacks empirical studies on long-term effectiveness.	No significant cybersecurity focus, although it highlights the need for secure digital platforms to protect student data.	NR
[56]	AI in e-learning systems	Explores AI technologies and applications in e-learning systems, including personalized learning, adaptive assessments, the automation of administrative tasks, and enhanced learner engagement.	Limited practical applications.	Discusses potential data privacy risks and issues related to security and data protection in AI-driven e-learning platforms.	NR
[57]	AIoT in assistive technology	A systematic review of AIoT applications in assistive technology, emphasizing innovations in healthcare, mobility aids, and smart living solutions.	No emphasis on challenges related to scalability and integration with existing systems.	Potential cyber issues include AIoT security vulnerabilities, data breaches, unauthorized access, and concerns regarding data integrity.	NR
[58]	AI assistive technology in hospital nursing	Designs a 3D human pose estimation system for home care robots, and how AI enhances professional nursing practices, including patient monitoring, diagnostics, workflow optimization, and decision support.	Lacks real-world case studies.	Security concerns related to patient data and medical data privacy.	R + NR
[59]	AI in prosthetic and orthotic rehabilitation	Reviews AI’s integration in rehabilitation technologies, including personalized design and functionality enhancements.	Limited cybersecurity perspective	No major cybersecurity risks are discussed, although connected prosthetic devices may be vulnerable to hacking or malfunction due to software flaws.	R + NR
[60]	AI in sports training	Explored AI applications in sports training through case studies, including optimizing athlete performance analysis, injury prevention, and training customization.	Lacks discussion of the practical implementation of AI in areas other than sports training.	Limited focus on significant cybersecurity issues, although wearable tech and AI systems used in sports could face threats such as data theft or system tampering.	NR
[61]	Emerging technologies in assistive tech	Reviews emerging technologies and their potential for creating new assistive technologies for people with disabilities.	General overview, lacks depth in AT applications and detailed implementation strategies.	Does not specify the types of cyber risks or mitigation strategies associated with emerging technologies.	NR

Table 1 shows the details of cyber-attacks and cybersecurity to analyze AI-based AT issues in the digital healthcare domain. It highlights the contributions of each paper, which address the relevant research issues focused on in this study. The limitations and summary help to identify the cybersecurity issues in digital healthcare applications.

According to [62,63,64,65], cardiac diseases are linked to diabetes, which affects the eyes and vision across all age groups, with some individuals becoming blind due to the direct impact of diabetes and heart diseases. Therefore, these issues must be proactively identified before developing an AI-based AT e-learning system for blind people, as health monitoring through medical devices and administrative processes are key aspects of digital healthcare applications. Due to potential cyberattacks, the medical devices used to monitor and detect these conditions may malfunction or produce inaccurate readings, which can compromise diagnosis and treatment. Understanding these issues allows researchers and scientists to assess the cyber risks in digital healthcare applications. The use of quantum technology in healthcare is being investigated for its potential to improve many issues, including the robustness of cybersecurity in digital health, which is a particular focus in sports education. For instance, blind people use smart AI-powered sticks [66,67,68], which provide many facilities to AT users of e-learning. In the most recent Paralympic Games, the use of AT was considered in many aspects of sports, along with emerging technologies such as AI-based quantum technology [69,70,71,72,73,74], which supports AT users with quantum sensors and routing. In further developments of 6G, AIoT and quantum technology will be used and considered in future research. When quantum technology becomes more mature, the AI-based quantum systems will be developed and implemented to improve future digital health.

3. Research Methodology

According to [6,7,8,9,10,11,12,13], we can use the model we developed in this study to help assistive users improve their lives through the modern and flexible AT and assistive systems considered in digital healthcare. Although e-learning is an example that has allowed for assistive users, the model presented in this research improves the robustness of the security features using a proactive approach.

3.1. The Assistive Systems

This article focuses on analyzing current and future assistive systems developed from the evolving and emerging AT. As an example, the research will show some relevant analyses of AT and secure communication for AI-based e-learning. The traditional AT used for e-learning faces many security issues and challenges to the potential communication infrastructure and academic networks that support the people who need these special devices. Advanced AT for e-learning provides many features which allow them to act according to their needs during daily issues and personal challenges. In many sectors, people with special needs use different systems according to their conditions. For instance, people who are visually impaired use AI-based tools such as touchpads, smart canes, and electronic spectacles, which enhance their e-learning capabilities.

AI-based spectacles: In the analysis of future AT, researchers can develop many features of traditional AI-based spectacles because demands on them are increasing due to busy lifestyles and changing environmental conditions. According to [66,67,68], AI-based smart sticks are devices for blind people that include many components. These components, such as environmental sensing and directional guidance, allow blind people to walk anywhere alone. With secure and AI-based sensor communication, people can even read signs and notices using sensors that convert them into an audio format. In healthcare, IoT already offers many facilities to AT users, including blind people, via smart IoT-based sticks. To improve digital health, AIoT will enhance monitoring and other facilities, such as e-learning, through smart sticks, where users touch the objects they want to learn about and understand.

3.2. Cybersecurity with Digital Healthcare Applications

Digital healthcare applications using AT in any assistive systems are connected to internal and external networks, including the Internet and emerging network technologies. Therefore, we cannot avoid cyber-attacks randomly occurring, derived from many different sources, when e-learning is used during special needs education to provide a demonstration of the healthcare training process, which is an administrative part of digital healthcare.

3.2.1. Assistive Systems for People with Sight Impairments

These systems may be affected by accidental attacks facilitated by blind users and their e-learning activities. Still, these eye- or sight-related problems can be resolved using AI-based assistive systems, which provide many proactive protections. Due to their external connectivity, targeted attacks are also possible, potentially compromising internal assistive devices, networks, and services. To improve blind people’s academic life, AI-based smart sticks can support them when they travel alone.

3.2.2. Assistive Systems for People with Hearing Impairments

Hearing aids are available with traditional and modern technologies to improve hearing issues and impairments. Modern spectacle frames equipped with AI-based hearing devices offer excellent auditory support, along with several advanced features that enhance learners’ experience in e-learning virtual environments.

3.2.3. Assistive Systems for People with Cognitive Impairments

People with cognitive issues need wearable devices such as helmets, to allow for them to go about their daily lives as normal. Brain communication systems are affected in brain disorders dependent on neuron communication, and can be controlled through AI-based digital healthcare management systems. Their security requirements vary according to the technology used.

3.2.4. Assistive Systems for People with Physical Impairments

People with physical disabilities and impairments may need additional external body parts, which could provide either traditional support or AI-powered support. When people started using robotics and AI-powered supports, security became an issue because medical communication is observed and monitored through continuous learning devices. Digital healthcare and e-learning must be protected from all evolving risks and cyber-attacks created by hackers who intentionally misuse AI.

3.3. Illustrative Use Case: Cyber Risk in AT-Based E-Learning for Blind Users

• Scenario:

Blind individuals often rely on e-learning platforms as part of their academic lives. These platforms are typically delivered through Virtual Learning Environment (VLE) systems, which are connected to both internal and external networks. To ensure accessibility, VLE tools must integrate assistive technologies (AT), such as AT-based systems, that enable effective communication between all users, including those who are blind.

However, these users are increasingly exposed to various cyber threats, including ransomware attacks, particularly when engaging with the administrative components of digital healthcare systems through internet-connected services. This scenario is relevant when analyzing the proposed research approach, particularly in the context of AI-driven risk evaluation.

• Medical Context in Digital Health:

According to [75,76], the healthcare tools used in eye care services must be securely designed, as they often incorporate advanced computing technologies, including AI algorithms and communication protocols. While these emerging technologies offer great potential, they also introduce significant cybersecurity risks. One such example is the Spectre attack—a vulnerability at the microarchitectural level of computer hardware that enables unauthorized access to sensitive data stored in device memory, including health records and AT-related information.

Furthermore, assistive technologies embedded with Fuzzy Expert Systems can be used to assess the risk levels for conditions such as heart disease and diabetes. These systems also represent a cybersecurity risk vector when deployed in AT-based e-learning platforms used by blind individuals.

As noted in [77], AIoT can significantly enhance future healthcare, especially as cybersecurity becomes a key feature in 6G developments, including 6G-IoT and 6G-AIoT. AI-based IoT will support the intended goals through advanced algorithms, protocols, and policies related to assistive technologies (AT) and their applications—such as e-learning for blind users. As AI-based algorithms continue to evolve within post-6G developments, the maturation of quantum technology is expected to further strengthen cybersecurity solutions and contribute to a 99.999% improvement in the overall performance of digital healthcare applications.

By aggregating the predicted cyber risks across digital healthcare and assistive technology applications, researchers can develop a proactive risk valuation model—as proposed in this study—to strengthen cybersecurity defenses. These risks include, but are not limited to, data breaches, unauthorized access, and potential failures of medical devices.

3.4. Theoretical Model

With the traditional and existing research in the literature review, a theoretical model of an AI-based wearable assistive system for e-learning could be designed with the necessary AI-based e-learning protocols and algorithms to support both assistive users and assistive system providers. As in Figure 3, the model developed in this study will allow us to enhance the robustness of cybersecurity solutions in AT- and AI-based assistive systems. It also presents the overall implementation of a secure AI-based infrastructure in AI-based designs of AT and assistive systems. In this generic theoretical model, risk calculation procedures can be added to an AI-based assistive management unit, which allows us to detect the cyber risks in AT users’ systems when blind people use the e-learning facilities. Risk solutions with AI-based processing (Figure 4) can be considered to detect and mitigate the cyber risks in an e-learning environment. In this proposed model, the AI algorithm module holds many AI-based algorithms related to AT, which includes AIoT. In future, we can add AI-based quantum algorithms and protocols as well.

3.5. Methodology

The main objective of this research is to analyze the cybersecurity vulnerabilities to minimize cyber risks and cyberattacks in assistive systems. Communication between the assistive systems depends on the AT and services the users handle. The main objectives of this research are the following:

• Learning potential risks, future cyberattacks, and threats with the evolving security issues considered in assistive systems with emerging technologies. The vulnerabilities of assistive systems that damage users’ services, applications, etc., have to be analyzed with the AT.
• Building an efficient model for securing assistive systems and services between the user devices that are fixed for those who use secure facilities, and analyzing the model presented in this study and the use of AT by those with physical disabilities.
• Developing cybersecurity solutions using security algorithms, intelligent techniques, and the latest emerging technology, aiming to increase security and reduce overall cost, depending on the energy consumption and use of low-complexity AT.
• Maintaining the dynamic cybersecurity solution while the services are used by different assistive systems.

A combination of selected protocols and AI-based algorithms from existing AI-based assistive systems and security models were considered in the development of an appropriate method for enhancing the cybersecurity solutions used in assistive systems. In the proposed approach, the AI-based security algorithm can be deployed to improve security performance at an affordable cost.

To measure risks, including ransomware threats, healthcare organizations must attempt to understand the attackers’ behavior using the Cyber Kill Chain (CKC) framework. In proactive models, CKC can be used to quantify the expected losses resulting from downtime or reputational damage.

Moreover, it also considers the implementation of information security governance and other cybersecurity measures deployed by an organization—such as data encryption and network segmentation—that could disrupt or neutralize the CKC.

As shown in Figure 4, the proposed cybersecurity system can be implemented and analyzed in all e-learning systems that support disabled blind students who depend on AI-based AT-related devices. Vulnerability assessments in these systems are critical to determine the risk of a ransomware attack. In Figure 4, an AI-based processing for cyber risks is considered, which has many possible steps, such as introducing types of risks. Identifying risks, including ransomware risks, is important, and ransomware risk mitigation can be achieved through the assessment and quantification of risk.

This study contributes to cybersecurity risk analysis by examining the risk regarding ransomware in digital healthcare applications connected to internal medical devices and external communication devices. Figure 4 can be used to analyze all risks, including ransomware risks, and carry out the necessary assessments, quantification and mitigations.

Using this model, one cybersecurity issue is considered, using the example of blindness and how blind people and their academic development are affected when they use e-learning. This methodology focuses on risks related to ransomware and how we can minimize these risks using the proposed model, which assesses, quantifies, and mitigates ransomware risk based on the concepts of threat appraisal and coping appraisal in protection motivation theory (PMT). The proposed model is based on AI concepts, which include PMT and AI-based cybersecurity protocols and algorithms. The model carries out the following steps:

• Biosensors collect the necessary data from assistive users and remote monitoring systems and send them to a wearable health monitoring block.
• Security alarm signals detect all types of cyber-attacks, including ransomware attacks, when data arrive at the AI-based assistive management unit.

The methodological steps presented above allow us to assess the risks in different time frames, determine the proactive stages of these cyber-attacks, and detect and mitigate future ransomware attacks. In ransomware risk assessment (Figure 4), the following factors allowed us to add a risk assessment procedure to the proposed model (Figure 3):

• Critical Industry (CI): In digital healthcare and learning environments, hackers install ransomware payloads on the CI’s e-health and e-learning infrastructure using phishing emails and vulnerable online systems. The AI-based assistive management unit (AAMU) identifies and detects ransomware attacks from phishing emails.
• Organization Size (Size): Big data analysis and control systems identify the size of the data and security control issues, such as policies, before AAMU determines the risks through AI-based data centers.
• Digital Intensity (DI): AAMU’s AI algorithms reduce the DI, which refers to the extent of digital technology usage across different levels of operational and strategic activities within a digital healthcare organization. When the proposed model allows us to reduce DI, ransomware attacks can be reduced.
• Network Segmentation (NS): The NS of big data in AI-based data centers helps minimize the occurrence of ransomware attacks.
• Attack Vector (AV): The AI-based security algorithms used in the development of perimeter elements will help prevent ransomware AVs from digital healthcare organizations.
• Privileges Required (PR): AAMU’s proactive approach to vulnerability and ransomware risk assessments depends on the use of AT and PR, which are less exposed to the risk of a ransomware attack.
• Attack Complexity (AC): AI algorithms and control systems identify the AC of IoT and biosensors because the scalability of IoT is increasing in assistive systems and digital healthcare organizations.
• User Interaction (UI): The proposed model’s remote monitoring collects the risks of UI when AT users are involved in e-learning and digital healthcare applications.
• Scope (SC): Cloud-based computing, secure communication channels, and alarm signals can reduce the probability of ransomware attacks. Handling policies, rules, and hypotheses that do not engage the UI and or change the authorization rights affect the SC of the vulnerable. Therefore, ransomware risks can be minimized using vulnerability assessments.
• Confidentiality–Integrity–Availability (CIA): The rules regarding security management in healthcare improve cybersecurity solutions by providing the necessary security algorithms, including CIA policies.
• Cybersecurity Role (CSR): The presence of a CSR within healthcare organizations ensures the proper implementation of IT controls and AT developments, and ensures the systems are used correctly, thereby enhancing cybersecurity.

According to [51], all modified hypotheses are considered for future testing with the real-time risk measurements used in AI-based AT systems:

H1a. 

Healthcare data communication in AT development organizationsis vulnerable tocyberattacks, including ransomware attacks.

H1b. 

Healthcare data management in AT-based sports industries is vulnerable to cyberattacks, including ransomware attacks.

H2. 

The big data used in digital healthcare management in AT-based financial and confidential transactions have a higher probability of facing ransomware attacks.

H3. 

External links involved in AT-based industries are more vulnerable to cyberattacks, including ransomware attacks.

H4. 

Healthcare security management in AT-based industries involved in information security governance is vulnerable to cyberattacks, including ransomware attacks.

H5a. 

The probability of ransomware and non-ransomware attacks increases with healthcare cost and the modern applications involved in AI-powered digital processing (Weibull distribution).

H5b. 

The expected cyber risks and AI-based data leaks/breaches arising from ransomware and non-ransomware attacks follow a gamma distribution.

H6a. 

If the healthcare organization faces a high probability of a high-severity ransomware attack, all types of risks should be considered to prioritize investments in cybersecurity technologies to ensure the secure development of e-learning.

H6b. 

If the healthcare organization faces a low probability of a high-severity ransomware attack, all types of risks should be considered to prioritize investments in AI-based cybersecurity management for the secure development of e-learning.

H6c. 

If the healthcare organization faces a low probability of a low-severity ransomware attack, all types of risks should be considered to prioritize investments in cyber insurance and appropriate mitigation for the secure development of e-learning.

H6d. 

If the healthcare organization faces a high probability of a low-severity ransomware attack, all types of risks should be considered to prioritize investments in AI-based security management with privacy policies for the secure development of e-learning.

3.6. Security in Digital Healthcare with E-Learning

Security in the current digital healthcare systems shows that all medical devices, including the assistive systems associated with the networks, IoMT, and medical infrastructure, are secure and safe from internal and external cyber-attacks. However, recent ransomware attacks in London hospitals have raised many questions and challenges about proactive solutions and building proactive systems. In the proposed approach, the e-learning module enables proactive systems to learn from all potential attacks that assistive users may accidentally cause while using their devices and systems. As shown in

Table 2. Basic digital healthcare with some random levels of risk.

Without Any Risks	With Various Risks
	Use of AT Tools	Diagnosing Symptoms on Time	Use of HealthcarePolicies	Securing Healthcare	Relying on Power and EE
Digital healthcare (medical)	1 (ii)	2 (i)	3 (ii)	4 (iii)	5 (i)
Digital healthcare (admin)	6 (i)	7 (ii)	8 (i)	9 (i)	10 (ii)

, security levels can be determined based on the specific situation and application. The table outlines basic risk calculations, both with and without certain random risks in digital healthcare applications, covering both medical and administrative aspects. Using Equation (5), the calculations consider basic impact levels—low (i), medium (ii), and high (iii)—along with ten likelihood factors for risk calculations. The following categories were used to analyze the risks in the specific AI-based AT security management for digital healthcare in our presented example:

• Low = (i): Minimum level of risk.
• Medium = (ii): Medium level of risk.
• High = (iii): Peak, maximum, or dangerous levels of risk.

When a qualitative analysis is conducted, the levels Low, Medium and High can be used for basic risk assessments. To improve the risk analysis with AI-based algorithms and protocols, we can add more levels of risk. Table 2 allows us to calculate a basic example of risk, as shown below.

The overall risk depends on the impact values (Low, Medium, and High) and index values (1 to 10) of the risks. In medical issues regarding digital health, the risk to secure healthcare is 4 × 3 = 12. In admin issues regarding digital healthcare, the risk to secure healthcare is 9 × 1 = 9.

These security levels allowed us to design a proactive module in our proposed model to mitigate these risks and other cyberattacks, including ransomware.

Improvements in digital healthcare depend on the use of AI-based systems and their processing, which include the technical and management issues faced within digital healthcare applications such as AT e-learning.

Table 3. Selected risk types (RTs) and digital healthcare applications for risk calculations.

Security Parameters for AT Users		Severity Levels of Blindness
		1	2	3	4	5
Patients’ service failure	Small	Social care (ii)	Assistive System (iii)	Residential care (i)	Vision clinic care (iii)	E-learning care (ii)
	Medium	Use of the latest tools (i)	Mobility training (i)	Use of AT (iii)	Support with training (i)	Paralympic care (iii)
Maintenance issues of AT (RT1)		Software (ii)	Hardware (iii)	Network issues (ii)	Maintainability (i)	Accessing e-learning (ii)
Use of AI-based eyeglasses (RT2)		Short-sighted (ii)	Long sighted (ii)	Blurry eye (iii)	Permanent eye disease (iii)	Blindness (iii)
Health and safety compliance (RT3)		Reliability (i)	Privacy (ii)	Safeguarding (i)	Health policies (ii)	Physical attacks (iii)
Food safety regulations (RT4)		Warm (ii)	Mild (i)	Cold (ii)	Hot (iii)	Pollen (iii)
Technological obsolescence (RT5)		Internal (i)	Emerging (ii)	External (iii)	Internet (iii)	Advanced (iii)

presents the hazards and negligence issues associated with digital healthcare applications, specifically focusing on selected assistive technology (AT) users. It highlights five risk types (RTs) considered in the security parameters for AT users, particularly those related to eye problems, including blindness, which may result from underlying conditions such as type 2 diabetes or cardiac disease in diabetic patients. For instance, consuming sugary foods increases the risk of both heart disease and diabetes-related eye complications. The risk levels, based on commitment to food safety conditions, range from warm, mild, cold, and hot to pollen, with each assigned an impact value according to its potential harm. In Figure 5, RT3 and RT4 illustrate the improvements achieved, which were influenced by the consumption of sugary foods and lack of health management. All necessary calculations can be performed using AI to enhance the robustness of cybersecurity in AT for digital healthcare services and applications. This means that risk-free or minimal risk levels can be maintained during the development of AT for blindness. Cybersecurity in AI-based AT and systems in digital healthcare depends on managing cyber risks. However, this can be complex, as analyzing all cyber risks requires consideration of data handling by medical devices, the services provided, and administrative issues related to diagnosing medical symptoms.

In addition, Figure 5, using RT5 as an example, demonstrates that the proposed model yields consistently lower risk scores than both traditional and AI-based assistive technologies. Based on the impact values presented in Table 2, the results highlight the proposed model’s measurable advantage in managing cybersecurity risks across digital healthcare systems.

4. Results

Cardiac issues can lead to internal disabilities, significantly impacting individuals’ daily lives and limiting their ability to perform regular tasks. For example, some children are born with fewer than four heart chambers and require AT supported by advanced AI-based devices.

Monitoring heart function and blood pressure remains essential for maintaining the health of all organs and their internal communication. In some cases, blindness may be linked to these internal issues, as the eye—being a sensory organ responsible for vision—can be affected by impaired internal communication. Children born with a single heart chamber may need an artificial heart to live, and those with visual impairment or blindness may require assistive support.

To better understand the underlying causes of blindness, cardiac symptoms can be analyzed using quantum technologies. These technologies provide insights into the communication between brain neurons, which depend on proper blood flow. Some neurons in the brain receive energy via blood supplied by nerves connecting the brain and the heart.

The primary goal, defined at the outset of the research project, is to analyze the symptoms of cardiac issues using AI and quantum technologies through the framework developed in this study. This research measures the prediction signals and the level of symptoms related to cardiac issues that are displayed. These levels can help provide benefits to patients and medical surgeons, including service providers. Comparing all existing AI-based proactive systems, our research and framework will bring more benefits because the combination of AI and quantum technology will improve the speed of all remote measurements when wearable continuous monitoring devices are used, even at more distant locations. It achieves the multiple goals of promoting the fundamental science needed to understand and establish basic AI and quantum principles for designing and architecting such systems, and further instantiates these principles with specific examples.

The results of the work presented in this study, focusing on digital healthcare systems, are expected to have the following points of utilization/impact:

• Theoretical and technical foundations for improving measurements related to cardiac issues using a proposed framework that will encourage researchers to explore the use of AI-based quantum technology in other specific areas involving cardiac issues in digital health.
• Practical AI-based quantum monitoring tools for developing a proposed framework and applying tested AI algorithms and quantum protocols.
• Extensions of existing software-based protocols related to cardiac issues (such as traditional routing and quantum routing protocols) that account for the dynamically changing time response versus risk and security levels in the critical, harsh digital healthcare environment, which could enhance the efficiency of proactive systems.

Figure 5 shows the total risk when making decisions before ransomware attacks because ransomware attackers learn the environment thoroughly. Therefore, risk analyses will allow us to monitor the necessary details. In Figure 5, we use the expected results of an “expected improvement of risk types in digital health” because did not use real quantum-based simulations, which would provide exact results for our research. Each hypothesis must be considered separately to analyze the cyber risks [13,14] that appeared during healthcare applications, such as blind users of e-learning applications. Considering our specific hypothesis, five risk types are presented in Table 3, along with severity levels of blindness. Although the proposed model is considered alongside emerging technology and algorithms (quantum, AI-based, etc.), some specific AI-based algorithms with basic quantum sensors were used in this model. With the real quantum simulation, the project’s primary goal was to achieve an analysis of AI-based AT and systems involved with digital healthcare (cardiac issues used to diagnose symptoms of blindness). Three approaches are compared and analyzed in this research: traditional AT, AI-powered AT, and the proposed approach. This allowed us to analyze and compare these approaches using the selected risk types and different hypotheses (assuming the first five hypotheses are considered). Using AI-based quantum sensors and other quantum technologies alongside the proposed model, we can improve the robustness of cybersecurity in AT.

Table 4. Examples of digital healthcare sources with ransomware risks.

Digital Healthcare Sources	Remarks of the Results		Methods for Ransomware Risks
	Contributions	Limitations
Biosensors	Offering secure communication between patients and doctors	Restricted with applications that leads to data breaches	Assessment with malicious intruder
Healthcare data	Investigating health risks quickly and accurately	Real-time data handling for proactive issues	AI-based security algorithms in mitigation
IoT devices	Handling remote and secure healthcare issues	Wireless risks in IoT networks linked with external devices	Mitigation
Assistive users	Availability of digital healthcare information, including e-learning with AT	Privacy of sensitive data should be ensured.	Assessment and phishing attacks
Energy	Providing secure power to all digital healthcare devices	High latency and delays experienced during operation	Quantification with AI-based cybersecurity

shows the ransomware risks that should be detectable when using the AI-based security algorithms or AI-based risk analysis introduced in this research and considered in our proposed model. It highlights various sources and types of ransomware threats that may arise in digital healthcare applications, particularly during the transmission of health data between sources and destinations.

The proposed work may expand the prediction measurements of the current features used in traditional proactive systems. Thus, the operational possibilities of important prediction scenarios involving AI-based quantum proactive systems and wearable continuous devices for autonomous healthcare services are widened. AI-based quantum technology, such as quantum sensors, can be integrated with many systems used by those with physical disabilities.

In this study, we consider a proactive approach to minimizing ransomware attacks using the predicted risks determined for different time frames.

This proposed model can monitor previous cyber-attacks in e-learning systems that were attacked by advanced persistent threats (APTs) and AI-based APTs.

Although many types of end-point security (EPS) software are considered in large organizations, ransomware attacks affect assistive systems and enter other major networks because vulnerabilities are unavoidable when AT users are involved in the assistive systems.

Assessing academic learning capacity depends on the assistive users’ mental stability and thinking power. Quantity and quality analyses of risk can be carried out with attention to the assistive users’ health conditions. Risk can be managed proactively through the proposed model, allowing for assistive users to address their health issues. Following mathematical Equations (1) and (2) will enable us to conduct a quantity analysis of risk:

Risk (R₁) = Hazards (H) + Negligence (N)

(1)

Risk (R₂) = Threat (T) + Vulnerability (V)

(2)

As shown in Equations (1) and (2), risks to all aspects of AT and AI-based AT systems should be considered and analyzed to improve the robustness of AI-based cybersecurity solutions.

In most AI-based AT and assistive systems, risk, as in Equation (3), can be determined by adding Equations (1) and (2) because future cybersecurity issues will involve a combination of these parameters:

$$\mathrm{R}=R_1+R_2=\mathrm{H}+\mathrm{N}+\mathrm{T}+\mathrm{V}$$

(3)

When we use AI to improve the overall security solutions in AT systems, all types of risks need to be considered.

Equation (1) is the traditional means of measuring risk in scenarios including the digital healthcare environment. The AI-based assistive systems will be integrated with the modified version of Equation (1), which allows for the improvement of digital healthcare applications, including AT. A modified version of this concept can be written as expected risk with respect to the time (t) of present and past hazards, as shown in Equation (4): Here, ${\mathrm{T}}_{\mathrm{C}}$ is a fixed time in the random period of data traffic.

R(t) = R(T_C − 1) + R(T_C − t)

(4)

The improvement of digital healthcare depends on not only management issues in digital healthcare but also on the risks to the overall systems. Qualitative risk analysis can help analyze risks that do not have a fixed cost implication, such as likelihood and reputational impacts, as presented in Equation (5):

Risk = likelihood × impact

(5)

A quality risk analysis can be conducted according to the likelihood and impact matrix defined below in Equations (6) and (7):

Likelihood = [Very Low, Low, Medium, High, Very High]

(6)

Impact = [None, Low, Medium, High, Critical]

(7)

According to the index of matrices (6) and (7), risk can be determined to improve proactiveness, which reduces the occurrence of ransomware attacks, as in Equation (8):

$${\mathrm{L}}_{\mathrm{C}}={\displaystyle \frac{{\mathrm{p}}_{\mathrm{L}1}\sum _{\mathrm{i}=1}^{\mathrm{x}}{\mathrm{L}}_{\mathrm{i}1}+{\mathrm{p}}_{\mathrm{L}2}\sum _{\mathrm{i}=1}^{\mathrm{x}}{\mathrm{L}}_{\mathrm{i}2}+\dots +{\mathrm{p}}_{\mathrm{L}\mathrm{j}}\sum _{\mathrm{i}=1}^{\mathrm{x}}{\mathrm{L}}_{\mathrm{i}\mathrm{j}}+\dots +{\mathrm{p}}_{\mathrm{L}\mathrm{n}}\sum _{\mathrm{i}=1}^{\mathrm{x}}{\mathrm{L}}_{\mathrm{i}\mathrm{n}}}{\mathrm{n}\mathrm{x}}}$$

(8)

Note: In risk analysis, H, N, T, and V are used.

Risk types from 1 to n are used in Equation (8) to calculate the likelihood of risk. Here, we can measure the likelihood (${\mathrm{L}}_{\mathrm{C}}$) using probabilities (${\mathrm{p}}_{\mathrm{L}1} \mathrm{t}\mathrm{o} {\mathrm{p}}_{\mathrm{L}\mathrm{n}}$) that are influenced by H, N, T, and V, including risk type, which varies according to the AT context. Total risk will be determined according to the number of samples (n).

5. Discussion and Analysis

AI-based proactive systems for healthcare applications are being developed using many new approaches. To improve the future applications and communication devices used in health and wellness, a quantum research hub for healthcare can be launched, focusing on AI-based digital healthcare. The aim is to improve the health and wellness applications used by healthcare providers, including communication devices, at a minimum cost. Here, many aspects that contribute to health and wellness should be monitored, looking at various contributing elements (physical, social, etc.). Improvements would allow for cyber risks to be proactively secured [72] and removed [51] using these contributing elements. These improvements (cyber risks, the proactiveness of accurate predicting infections, etc.) could be achieved using quantum research with AI-based techniques.

Risk analysis depends on the quantitative and qualitative approaches considered when securing digital healthcare applications. Quantitative risk analysis focuses on the following issues within AI-based AT:

• Assets are assigned a value (asset value (AV)).
• Each asset is researched and a list is produced of all possible threats to each asset. The exposure factor (EF) and single loss expectancy (SLE) for each listed threat are focused on.
• A threat analysis is conducted to calculate the likelihood of each threat being realized within a single year—the annualized rate of occurrence (ARO).
• The overall loss potential per threat is derived by calculating the annualized loss expectancy (ALE).
• Research the countermeasures for each threat and then calculate the changes to ARO and ALE based on the applied countermeasure.
• Perform a cost/benefit analysis of each countermeasure

Qualitative risk analysis [51] allows us to determine the proactive behaviors of the ransomware attacks in the AT environments of digital healthcare. Samples of risk types can differ according to the AT digital healthcare users, the AT environment, and its applications. For instance, blind people use e-learning environments and digital healthcare procedures featuring AT or AI-based AT. In this situation, qualitative risk analysis allows us to proactively identify the cyber and ransomware risks. Therefore, this proposed model will improve the robustness of security in the AT environments in which assistive users go about their daily lives using e-learning facilities.

Table 5. Samples of risk types and qualitative risk analysis.

Variables	Risk Types of AT in Digital Health	Likelihood	Impact	Risk for 1 Sample
Critical industry (CI)	AT developers	High	Low	4 × 2 = 8
Organization size (Size)	Number of staff and AT users in any organizations	Medium	None	3 × 1 = 3
Digital intensity (DI)	AT users’ big data	Very high	Low	5 × 2 = 10
Network segmentation (NS)	Communication network of AT users	High	Low	4 × 2 = 8
Attack vector (AV)	Infrastructure with cyberattacks	Very low	Medium	1 × 3 = 3
Privileges required (PR)	Priorities of AT in digital health	Low	Critical	2 × 5 = 10
Attack complexity (AC)	IoT sensors network in AT applications	Medium	Low	3 × 2 = 6
User interaction (UI)	Availability of AT users in peak time	Very high	High	5 × 4 = 20

shows example risk calculations for a single sample with various applications, including the proposed digital healthcare issues. These calculations allow service providers to analyze potential improvements. The risks are derived from the basic types listed in Table 1. In this discussion, AI-based digital healthcare is considered, along with many variables that form the basis of various cybersecurity risks. Thus, using Figure 4, this research methodology can be modified according to the types of risks involved in digital healthcare. Hypotheses H1 to H6 in Figure 4 will be useful because the best hypotheses will improve the overall cybersecurity of AT used in digital healthcare. Ransomware attacks have caused most medical activities in the UK to stop for some days, and many similar attacks are still having economic impacts. Therefore, proactively identifying ransomware risks using Figure 4 can improve digital healthcare (for example, through improving diagnoses of blindness). Secure e-learning is one of the programs used by AT users with AI-based AT communication devices, which faces many risk types according to the AT sources.

Using Equations (6) and (7), the index of the matrix orders can be used to determine the risk for a single or individual sample.

To complete the orders, the likelihood and impact matrix orders’ indexes (representing 1 to 5) are given below:

• Likelihood = [Very Low, Low, Medium, High, Very High].
• Impact = [None, Low, Medium, High, Critical].

Waiting lists are pain points for target customers worldwide because the growing population and the rate at which infections spread cause unavoidable issues. Health issues are increasingly facing elderly populations and mental health issues are increasing, along with other issues due to situations such as the aftermath of war, the fast-food culture, a lack of physical activity, etc. Quantum and AI-based techniques and algorithms, along with the specific prediction model, can be used to solve these issues.

6. Challenges

AI-based techniques and quantum research can create new challenges and solutions for healthcare products related to the risk of AI use in healthcare, cybersecurity solutions in healthcare applications, and the communication devices presented in [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. These techniques and quantum approaches can enhance the overall facilities of digital healthcare and wellness, and they will undergo further innovative developments. In developing a product focused on the innovation of specific algorithms and protocols, the following key factors should be considered:

• Quantum sensing technology enhances the speed of response time which, allows for doctors to see symptoms proactively when diagnosing using miniature devices.
• The first biological quantum-light microscope used squeezed light in an optical tweezer to probe the interior of a living yeast cell.
• The Integrated Quantum Networks (IQN) Hub is a research hub that aims to create a quantum internet.
• Advanced quantum communication protocols can improve medical systems by providing efficient and secure communication and the efficient and secure administration of digital healthcare.
• Quantum routing will upgrade the traditional features integrated with the legacy assistive systems used in digital healthcare.
• Quantum position navigation and timing (PNT) can be used in the prediction models used to diagnose heart diseases through the insertion of miniature devices.
• Miniature microfabricated quantum inertial sensors can be used to examine ex neuron communication within the brain and other organs using specific miniature devices.

The innovations mentioned above will be developed and implemented in this research, which will support our stakeholders in improving the health and wellness applications of existing products and services.

Quantum technologies harness quantum physics to achieve a functionality or performance that is otherwise unattainable, deriving from science that cannot be explained by classical physics. The dynamics of neuron sensors can be further developed using quantum sensors and PNT. As is detailed below, biomedical and diagnostic issues can be addressed using miniature AT devices [69]:

• Biomedical Imaging: Quantum sensors that detect minute variations in magnetic fields are being created for a variety of uses, including the early detection of Alzheimer’s disease, cardiovascular disease, and epilepsy. Faster scan speeds to help reduce waiting lists are among the new quantum imaging features being investigated to improve MRI.
• Quantum-Enhanced In Vitro Diagnostics: Spin-enhanced nanodiamond sensors will be used to develop ultra-sensitive blood tests in portable formats, such as basic lateral flow testing. This could allow for the early diagnosis of a variety of diseases, from infections to cancer, by expanding access to testing in GP offices and pharmacies, as well as at-home self-testing. Novel surgical and therapeutic approaches could also be provided for difficult-to-treat and early-stage malignancies. Among these is a novel strategy looking into the treatment of cancer with magnetic nanoparticles.

According to [24,69], communication hubs can be used to enhance the robustness of cybersecurity and enable researchers to identify cyber risks associated with the power sources and power systems used in assistive technologies (AT). These hubs can be integrated with quantum technology to strengthen AI-based AT systems—an initiative aligned with government ambitions, as this innovative approach to future AT could boost the economy. Furthermore, quantum technology allows researchers to develop new quantum-based biomedicine (Q-BIOMED) hubs to advance healthcare using technologies such as quantum sensors. By employing the proposed model within Q-BIOMED hubs, cyber risks can be identified quickly and proactively before AI-based AT systems are implemented in e-learning environments.

A new quantum sensing technology capable of analyzing single cells and molecules could help researchers identify mechanisms within cells that could be targeted to prevent disease. This could be used in the following ways:

• To allow for secure digital healthcare and rehabilitation.
• In e-learning paradigms for neural networks and automated medical diagnostics.

Quantum approaches will improve the robustness of miniature devices used for diagnosis with nano-computing networks and miniature detectors (MD).

Although quantum research is still in its early stages and faces many obstacles, it holds revolutionary potential for digital healthcare applications. Consistent funding, interdisciplinary cooperation, and the creation of strong regulatory frameworks are needed to address these constraints. These obstacles could be overcome using quantum technologies, opening the door to previously unheard-of breakthroughs in fields like tailored medicine, secure communication devices, and AI-driven diagnostics. Some challenges and limitations are as follows:

• Hardware limitations, such as limitations to its scalability, high error rates in calculation, and cryogenic requirements.
• Quantum algorithms are limited and their integration with AI is still in its infancy.
• Quantum threats to encryption threaten data privacy and security, requiring the adoption of quantum-safe cryptography.
• The expensive infrastructure, resource intensity lead to high costs and resource constraints.
• Data bias in quantum AI and regulatory uncertainty lead to ethical and regulatory concerns.
• The shortage of skilled professionals and collaboration barriers between quantum researchers and healthcare practitioners lead to gaps in interdisciplinary expertise.
• There is uncertainty in its practical applications due to the limited real-world use cases and long development timelines.
• Its integration with existing systems faces compatibility issues that require hybrid solutions.
• Examinations of public perception and trust suggest a lack of awareness of many healthcare stakeholders, as well as trust issues and implications regarding its reliability, transparency, and ethical use.
• The use of AIoT with IQN will lead to many new challenges when a priority approach is considered in digital healthcare and AI-based AT.
• Miniature microfabricated quantum inertial sensors and PNT will enhance the proactiveness of the prediction models. Quantum routing in neuron sensor communication will allow doctors to monitor the weakest neuron behaviors, which lead to health data showing risks that were found in the organs.

7. Conclusions and Future Work

AT is explained using the example of e-learning, which supports assistive users when digital healthcare is developed using AI. This can enhance the listening and observing capacities of people with health conditions such as brain disorders.

When wearable health-monitoring with AI and AT for e-learning is active, cyberattacks are expected, but these can be detected to prevent damage to the whole assistive network and traditional management units. e-learning supports both digital healthcare and assistive systems because e-learning can also be used for the administrative side of healthcare, medical communication, etc. All assistive systems, including those in traditional, AI-based, and quantum digital healthcare facilities, can be enhanced through e-learning.

Three approaches (traditional AT, AI-powered AT, and the proposed AT with emerging technology) are compared and analyzed in this research. When quantum technologies and algorithms are employed as emerging technologies in the proposed model, AI-based AT can be improved because the proposed approach can proactively detect risks.

Using the theoretical model with modified hypotheses [51], we can maximize the e-learning capacities through AI-based cybersecurity policies when implementing AI-powered assistive devices and systems. We will also test our theoretical model and verify the results regarding e-learning capacities using the different types of cyber risks collected from different assistive systems, as well as comparing these with those of traditional and AI-powered assistive systems. Using the same risk parameters, the theoretical results of the proposed research show that the AI-based proposed model is improved.

Future work could implement some modifications, such as the real-time implementation of the model with real-time data from digital healthcare organizations. The AI-based AT concept could be used to develop AI-powered shoes for people who are unable to walk normally. Those who face difficulties walking uphill could wear AI-powered shoes, and the heels could be adjusted to ensure the body remains straight and perpendicular to the shoe platform. Those with breathing problems could wear AI-powered shoes that would allow them to walk horizontally and support them in regular walking exercises.

The proposed work could be extended to different forms of AI-based AT, such as devices used for people with brain disorders who desperately need AI-based quantum technology that enhances neuron communication features. With quantum technology [69,70,71,72], quantum-routing approaches can help address the issues caused by brain disorder through the deep learning process of risk analysis and external controls to improve functioning.