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The rapid expansion of telemedicine, driven by the COVID-19 pandemic, has highlighted persistent inequities in access, particularly among underserved populations. This chapter explores the digital health divide and how artificial intelligence (AI)-driven telemedicine, combined with policy reforms, can help bridge these gaps. The telemedicine equity framework (TEF) is introduced as a practical tool for policymakers, researchers, and healthcare system designers to assess and address digital health disparities. The TEF focuses on four core areas: technology readiness, infrastructure access, policy environment, and community capacity. It aims to provide a comprehensive approach to ensuring equitable access to telemedicine services. The chapter examines key barriers, including broadband gaps, digital literacy, and healthcare workforce shortages, and the ethical considerations surrounding AI applications in telemedicine. It highlights case studies such as AI-powered maternal health triage in rural Africa and mHealth solutions for tuberculosis adherence in India, which demonstrate the potential of digital health to reach underserved populations. Additionally, the chapter explores the role of blockchain in enabling secure, decentralized health records and discusses the policy challenges that arise from cross-jurisdictional telemedicine, such as licensing restrictions and data sovereignty. Policy recommendations emphasize aligning regulation with equity goals, supporting digital literacy initiatives, and fostering community engagement in digital health solutions. Through global case studies, the chapter illustrates the importance of inclusivity in designing and implementing telemedicine systems, ultimately contributing to universal health coverage and sustainable digital health futures.
Department of Medical Biochemistry, Faculty of Medicine, Girne American University, Karaman, Kyrenia, North Cyprus, Turkey
*Address all correspondence to: mthabisimoyo@gau.edu.tr
1. Introduction
The COVID-19 pandemic rapidly transformed telemedicine from a niche service to a core component of healthcare delivery [1]. Governments, health systems, and private innovators deployed virtual platforms to sustain care, reduce infection risks, and expand remote monitoring, while artificial intelligence (AI) tools, chatbots, and mobile health (mHealth) apps gained global traction [2]. Yet this digital shift revealed a troubling paradox: those most in need of care were often least able to access it [1, 3]. Despite progress, telemedicine remains inaccessible in many low-resource communities due to broadband deserts, limited devices, language and literacy barriers, and workforce shortages [4]. Globally, 2.6 billion people lack Internet access and 3.4 billion lack reliable connectivity, challenges that disproportionately affect rural, remote, Indigenous, and marginalized urban populations [5]. These divides reflect not only technical gaps but deeper systemic inequities rooted in historical neglect and policy inertia [5].
To avoid worsening disparities, digital health must be intentionally designed to advance equity. The telemedicine equity framework (TEF), a novel, interdisciplinary tool, offers a practical approach for assessing and implementing equitable strategies across four domains: technological maturity, infrastructural access, policy environment, and community capacity.
This chapter reimagines digital health through an equity-centered lens, exploring how technologies like AI, blockchain, and mHealth can close not widen existing gaps. It examines barriers to access and highlights inclusive innovations, offering not just an analysis of inequality but a blueprint for change. By uniting innovation, policy, and lived experience, it envisions a digital health future where no one is left offline, unseen, or unheard.
Despite the surge in telemedicine usage during and after the COVID-19 pandemic, millions remain excluded from its benefits [3]. The “digital health divide” represents more than a technological gap, and it encapsulates deep-rooted disparities shaped by socioeconomic status, geography, education, culture, and identity [6]. To bridge this divide, we must understand the layered nature of exclusion that limits healthcare access in underserved populations. Technological barriers, cultural mismatches, and intersectional inequalities all contribute to the persistence of inequity, highlighting the need for digital health systems designed with equity at their core.
2.1 Socioeconomic and infrastructural barriers
In underserved communities, rural, remote, and low-income urban alike the absence of reliable broadband remains a primary barrier [7]. In sub-Saharan Africa, for instance, less than 30% of the population has consistent access to the Internet, and in rural areas of the United States (U.S.), 22% of adults report not using the Internet at all [8, 9]. However, Internet connectivity represents only one facet of the broader digital divide. This divide also encompasses limited access to essential digital devices such as smartphones, tablets, and computers; inconsistent or unreliable electricity supply; and financial barriers that prevent individuals from affording data plans or necessary hardware upgrades [6]. Table 1 illustrates a comparative matrix of how digital health barriers are distributed across rural, urban poor, and Indigenous communities. This highlights the overlapping nature of infrastructural and socioeconomic challenges that constrain equitable telemedicine access.
Digital health barrier
Rural communities
Urban poor
Indigenous communities
Broadband Internet Access
Severely Limited
Moderate Access
Severely Limited
Availability
Moderate Access
Low Availability
Limited
Reliable Electricity Supply
Unstable Supply
Moderate Supply
Unstable Supply
Basic Digital Literacy
Low Literacy Levels
Basic to Moderate
Low Literacy Levels
Language and Communication Accessibility
Partial Accessibility
High Need for Translation
Culturally Inaccessible
Trust in Health Technology Platforms
Low Trust
Cautious Trust
Historical
Table 1.
Comparative matrix of digital health barriers across underserved communities.
Technological innovation often assumes an “always-connected” end user, yet the reality is far more dispersed [10]. For many individuals, even the regular charging of devices presents a logistical challenge. These infrastructural limitations significantly influence the accessibility and feasibility of telemedicine services, determining which services are practically deployable and accessible to specific populations. The “last mile” of digital infrastructure is not merely a technical issue; it represents a critical equity concern, disproportionately impacting already singled out groups [11].
2.2 Cultural and educational gaps
Despite the availability of infrastructure, digital health tools continue to be underutilized due to significant barriers to language, literacy, and trust [12]. A large portion of the population faces challenges in acquiring the digital skills necessary to effectively navigate apps, patient portals, and virtual consultations [13].
In multilingual countries, many telemedicine platforms operate in dominant languages, inadvertently excluding speakers of Indigenous and Indigenous languages [14]. Additionally, there is a pervasive mistrust of digital technologies, especially within those affected by surveillance, and medical supervision [15]. If patients are concerned about potential misuse of their data or fear being misunderstood, they are less likely to engage with these platforms [16]. To promote meaningful engagement, it is crucial to develop digital systems that are culturally responsive, linguistically inclusive, and user-friendly [17].
2.3 The role of intersectionality in compounding exclusion
The digital health divide is characterized by disproportionate impacts across various demographic groups, with women, individuals with disabilities, racial and ethnic minorities, the elderly, and LGBTQ+ populations encountering intersecting barriers that exacerbate exclusion [18, 19]. For instance, Indigenous women in remote regions of Canada may experience compounded challenges, including geographic isolation, cultural mistrust of the healthcare system, and gendered expectations that restrict their autonomy or availability for seeking healthcare services [20].
Figure 1 below presents a visual model of how these intersectional factors—structural, cultural, and identity-based—overlap to intensify digital health exclusion. The diagram serves as a conceptual tool to recognize the compounding nature of disadvantage that must be addressed in any equitable telemedicine design.
Figure 1.
Intersectional factors affecting digital health exclusion.
Recognizing intersectionality is key to designing inclusive telemedicine solutions. Programs must move beyond a one-size-fits-all model to account for the layered vulnerabilities that shape digital health access. Without this lens, well-intentioned innovations risk reinforcing existing disparities rather than dismantling them.
3. AI-driven telemedicine: Opportunities and ethical imperatives
The integration of AI into telemedicine has revolutionized the landscape of remote health care, characterized by enhanced efficiency, scalability, and complexity [21]. AI-driven technologies, ranging from predictive analytics and diagnostic support to virtual counseling and remote patient monitoring, are fundamentally transforming the methods through which health care is accessed, delivered, and assessed. In resource-constrained environments, AI has the potential to amplify service reach, alleviate clinician workload, and enhance the precision of clinical decision-making [22].
However, despite the undeniable promise of AI in digital health, its implementation poses significant risks [23]. Inadequate system design, biased datasets, and insufficient regulatory safeguards may exacerbate the healthcare disparities AI seeks to address [24]. The analysis focuses on the dual nature of AI in telemedicine, its capacity to revolutionize care delivery, and the ethical dilemmas it introduces, while evaluating the current regulatory landscape that governs its integration.
3.1 Machine learning in diagnostics, triage, and personalized care
Machine learning (ML) models have transformed remote diagnostics and triage by enabling systems to “learn” from vast datasets of symptoms, images, and outcomes [25]. AI can rapidly analyze patient-reported data such as cough patterns, skin lesions, or risk factors and generate probability-based assessments [26]. For instance, in dermatology, ML models have achieved near-dermatologist accuracy in identifying skin cancers digital images [27, 28, 29]. In triage, AI can prioritize patients based on severity, flag high-risk cases for follow-up, and reduce time-to-treatment, especially in crisis contexts [22]. Furthermore, predictive modeling facilitates personalized care pathways. Algorithms trained on longitudinal health data can predict complications (e.g., diabetes-related vision loss) and suggest preventive interventions, potentially improving outcomes and reducing costs [22]. These functions are particularly valuable in resource-constrained settings, where clinical staff shortages and logistical barriers delay diagnoses [22, 30]. AI becomes not just an aid but a multiplier of health system capacity [30].
3.2 AI Chatbots and virtual agents in low-resource settings
Conversational AI via chatbots, short message service (SMS) agents, or voice assistants has found a niche in global health as a scalable, always-available support system [31]. These tools deliver mental health check-ins, maternal health guidance, medication adherence reminders, and even chronic disease management in multiple languages [22]. For example, in parts of Southeast Asia, WhatsApp-based chatbots guide TB patients through treatment regimens [32]. In refugee camps, AI-powered mental health tools offer anonymous emotional support, navigating stigma and language barriers [32]. Virtual agents require minimal infrastructure only a mobile device and intermittent connectivity making them ideal for low-income and rural settings. When integrated with national health systems, they can streamline referrals and feedback loops, improving service delivery at scale [33].
3.3 Ethical imperatives: Bias, transparency, and data privacy
AI in health care offers significant benefits but raises ethical challenges, notably algorithmic bias from training data over representing white, urban, or male populations, leading to inaccurate outcomes for other groups—such as pulse oximeters misreading darker skin tones [34, 35, 36, 37, 38, 39, 40]. Lack of explainability in deep learning models reduces trust in high-stakes decisions [34, 41], while data privacy risks persist, especially in regions without strong legal protections, risking misuse of sensitive health data [34, 42]. In response, global bodies provide guidelines: the World Health Organization (WHO) promotes transparency, inclusivity, and accountability [43, 44]; the European Union’s (EU) General Data Protection Regulation (GDPR) enforces data minimization and informed consent [45]; and the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD) advocate human-centered, rights-based AI ethics [46, 47]. Together, these frameworks help ensure AI protects patients, advances equity, and builds trust worldwide. Table 2 highlights AI telemedicine applications alongside their ethical and operational risks.
Ai opportunity
Illustrative application
Ethical/Operational risk
Real-world or hypothetical example
Enhanced Diagnostic Precision
AI-enabled image analysis for early detection of diseases (e.g., cancer and retinopathy)
Diagnostic errors due to biased or non-representative training data
Retinal scan misclassification in patients from underrepresented ethnic groups
Efficient Triage and Workflow Optimization
Natural language processing (NLP) tools for prioritizing emergency cases
Algorithmic opacity and lack of explainability
Clinicians may not trust or understand AI’s rationale in critical decisions
Remote Monitoring and Proactive Care
Wearables and smart sensors transmitting real-time vitals to clinicians
Data security and unauthorized third-party access
Breach of patient data from unsecured wearable devices
Expanded Access to Care
AI-driven chatbots delivering primary care guidance in underserved areas
Risk of mismanagement or misdiagnosis without oversight
Rural patients relying solely on AI for conditions requiring in-person evaluation
Reduction in Provider Burnout
Automation of administrative tasks, e.g., clinical documentation
Deskilling and reduced clinical intuition
Clinicians becoming overly reliant on AI-generated assessments
Cost Efficiency and Resource Allocation
AI tools streamlining appointment scheduling and resource deployment
Digital divide and unequal access to AI-enabled services
Advanced AI services concentrated in urban or private healthcare settings
Personalized Treatment Strategies
AI integration of genomics and electronic health record (EHR) data for tailored care
Ethical concerns over informed consent and data ownership
Use of patient genomic data in algorithm development without explicit permission
Table 2.
Opportunities and risks of AI in telemedicine — with illustrative examples.
4. Blockchain and digital infrastructure for secure, decentralized health records
As health systems digitize post-COVID-19, secure, interoperable infrastructure is essential for equitable care. Yet for billions, especially in underserved or displaced communities, telemedicine is limited without trusted, portable, and patient-controlled data. Health records often remain siloed or inaccessible [48]. Beyond cryptocurrency, blockchain offers a decentralized framework for health data governance, with potential to empower patients, enhance transparency, and rebuild trust [48, 49]. However, its impact depends on ethical use, equitable access, and contextual relevance. This section examines both the potential and limitations of blockchain in advancing digital health equity [50].
4.1 Blockchain for patient-controlled, portable, and verifiable health records
At its core, blockchain enables decentralized data stewardship, where individuals not institutions can securely manage access to their health records. Each medical transaction or update is cryptographically recorded across a distributed ledger, creating an immutable and verifiable history of care. For patients in low-resource settings, humanitarian crises, or mobile populations, this offers a pathway to consistent and portable health documentation—without dependence on a single facility or nation-state [51].
4.2 Smart contracts: Automating trust and consent across jurisdictions
In global telemedicine, legal ambiguities and data-sharing inconsistencies often undermine care continuity [44]. Smart contracts self-executing blockchain-based code can bridge this gap by encoding rules around informed consent, data access, or provider obligations. For example, a refugee patient could grant time-limited, read-only access to mental health records for a virtual consultation across borders, with consent revocable at any time [48]. This approach ensures granular, enforceable control over how health data is used and by whom particularly critical in regions with limited legal protections or digital literacy. Moreover, smart contracts can streamline billing, prescription validation, and referral workflows, reducing administrative friction and improving care coordination [49].
4.3 Challenges: Energy, equity, and infrastructure realities
Blockchain holds transformative potential for digital health but faces significant implementation challenges, especially in underserved regions with poor infrastructure and digital inequities [52]. High energy demands, particularly from proof-of-work models like Bitcoin, make it unsuitable in areas with unstable power, while more efficient alternatives like proof-of-stake remain impractical due to environmental and financial constraints [53]. Adoption is further limited by high costs, technical expertise requirements, and maintenance needs, which strain regions with limited infrastructure and health budgets. Without local capacity-building, blockchain risks perpetuating global inequities; sustainable success requires developing local skills and equitable partnerships [54]. Connectivity and digital literacy gaps also hinder use, as blockchain depends on Internet access, digital tools, and user competency—often lacking due to literacy, language barriers, or distrust [55]. Additionally, lack of global standards raises interoperability issues, risking new data silos within fragmented health systems and impeding data exchange [56]. Therefore, blockchain should be seen as a supportive technology integrated into broader digital health strategies emphasizing sustainability, inclusivity, and standardization. Table 3 summarizes its benefits and limitations.
Opportunity
Description / Example
Limitation
Description / Example
Data Integrity and Security
Immutable, time-stamped records ensure tamper-resistance, reducing risks of fraud and unauthorized access
Scalability constraints
Blockchain networks struggle with large volumes of health data, e.g., imaging or genomics
Interoperable Health Records
Decentralized access enables seamless sharing of patient data across providers and borders
Lack of standardization
Inconsistent data formats and blockchain protocols limit integration across systems
Patient-Centric Consent Management
Smart contracts allow patients to control access to their health data in real time
Usability and digital literacy gaps
Many patients may lack the skills or tools to manage blockchain-based permissions
Transparent Clinical Trials and Research Audits
Blockchain can log trial phases, data changes, and consent events, increasing trust and traceability
Regulatory ambiguity
Few jurisdictions have clear guidelines on blockchain use in biomedical research
Secure Pharmaceutical Supply Chains
Tracks provenance of medications, reducing counterfeiting and ensuring cold chain compliance
Cost of infrastructure integration
Linking blockchain to physical supply chains requires major upgrades in logistics systems
Fraud Prevention in Billing and Claims
Smart contracts and verifiable transactions reduce insurance fraud and billing errors
Institutional resistance
Insurers and intermediaries may resist systems that reduce their administrative control
Cross-Border Data Portability
Patients can carry their decentralized health records across countries, supporting care continuity
Legal conflicts with data protection laws
Blockchain’s immutability can clash with privacy mandates like the GDPR “right to be forgotten”
Table 3.
Advantages and limitations of blockchain in digital health.
mHealth solutions are transforming healthcare delivery in underserved communities by leveraging the widespread presence of mobile phones to improve patient education, treatment adherence, and real-time monitoring [57]. In areas with limited Internet and infrastructure, SMS-based mHealth tools offer a low-cost, scalable method for sending health messages and reminders. These have proven effective in boosting adherence for chronic conditions such as hypertension, diabetes, and tuberculosis [58]. Mobile apps provide more advanced capabilities—tracking health data, delivering personalized advice, and enabling direct communication with healthcare providers. When combined with real-time data and predictive analytics, they support proactive management of chronic diseases [59, 60]. However, mHealth’s success depends not just on technology, but on people. Community health workers (CHWs), trusted for their local knowledge and cultural fluency, are essential in integrating these tools into care systems. They educate patients, enhance digital literacy, and ensure the solutions are culturally relevant and accessible [61].
5.1 Case studies
In India, where tuberculosis (TB) remains a significant public health challenge, mobile health (mHealth) solutions have markedly improved treatment adherence and reduced default rates. Daily SMS reminders to TB patients have significantly increased adherence and lowered the default rate by up to 40%, enabling real-time support that helps prevent TB spread and drug resistance [62]. Similarly, in Canadian Indigenous communities with historically poor maternal and infant health outcomes, mHealth has been employed to monitor pregnancies and enhance access to maternal health care. These tools provide culturally tailored educational content, vital check-ins, and remote consultations, reducing preventable childbirth complications, ensuring regular prenatal visits, and improving maternal health literacy. The integration of culturally relevant interventions has also boosted patient engagement and trust, highlighting the importance of cultural sensitivity in digital health [63].
5.2 Path forward
While mHealth presents a powerful tool for improving healthcare access and outcomes, significant challenges remain. The digital divide is still a major barrier in many parts of the world, with millions lacking access to reliable smartphones, Internet connectivity, or even basic digital literacy. To address these gaps, there must be a concerted effort to expand digital infrastructure, such as affordable internet access, low-cost smartphones, and widespread digital literacy programs [64]. In parallel, healthcare systems must build local capacity to support these digital tools, especially by training and empowering CHW who can bridge the gap between technology and patients. Culturally tailored mHealth solutions are critical for ensuring that the messages, content, and interventions resonate with diverse populations, and that they are adapted to local languages, customs, and practices [61]. Finally, it is essential to ensure the sustainability of mHealth programs. Many mHealth interventions are introduced as short-term solutions or pilots, but for long-term success, these programs must be embedded within existing health systems and supported through ongoing funding and policy commitment [61].
6. Policy barriers and cross-jurisdictional challenges
As AI-powered digital health technologies advance, the main barriers to equitable care have shifted from technology to regulation. Outdated, fragmented, and jurisdiction-bound policies have become new social determinants of health, restricting AI-driven telemedicine’s potential to overcome geographic and socioeconomic disparities. Healthcare providers remain constrained by licensing systems tied to geography rather than capability; for example, physicians licensed in one U.S. state cannot legally consult patients in another without costly, burdensome re-licensing—a challenge mirrored in federal countries like Canada and Australia. Reimbursement policies often fail to cover telemedicine services equally, especially AI-assisted, or asynchronous care, disproportionately impacting rural and low-income populations who may have Internet but lack local specialists. Cross-border telemedicine faces additional hurdles including conflicting data privacy laws such as the European Union’s GDPR versus the U.S. Health Insurance Portability and Accountability Act of 1996 (HIPAA), creating legal ambiguity around data storage, patient consent, and jurisdiction. This is compounded by unclear malpractice liability and patient redress frameworks, discouraging providers from expanding care across borders and further marginalizing vulnerable groups. These challenges—spanning licensure fragmentation, data sovereignty, reimbursement gaps, and inconsistent privacy enforcement—are global in scope. Table 4 summarizes key telemedicine policy barriers alongside promising regional strategies to address them.
Region
Policy & regulatory barriers
Cross-jurisdictional challenges
Proposed solutions
United States
Fragmented state regulations
Inconsistent telemedicine reimbursement
Complex HIPAA compliance
State licensure limits interstate telemedicine
Varied definitions of telemedicine services
Expand interstate licensure compacts (e.g., Interstate Medical Licensure Compact (IMLC))
Harmonize patient data protection laws - Launch national certification for telemedicine providers
South & Southeast Asia
Outdated health laws - Exclusion from insurance schemes
Weak cybersecurity enforcement
Limited cross-national strategy for migrant access
Language fragmentation in platforms
Update national laws to include digital health
Coordinate Association of Southeast Asian Nations (ASEAN)-wide telemedicine standards
Scale inclusive, multilingual digital health tools
Middle East & North Africa (MENA)
Fragmented regulation across nations
Restriction on cross-border care
Conservative privacy laws
Lack of digital ID systems
Instability hinders regional frameworks
Align regulations with WHO digital health guidelines
Develop regional digital health alliances
Improve refugee access to cross-border telemedicine
Table 4.
Telemedicine policy barriers and proposed solutions by region.
6.1 Rollback of temporary COVID-Era flexibilities and aligning regulation with innovation
The COVID-19 pandemic temporarily transformed telemedicine through emergency waivers that enabled cross-state licensing, expanded reimbursement, and encouraged remote care, significantly improving access especially for public populations such as rural communities, people with disabilities, low-income patients, and communities of color. However, the rollback of these flexibilities now threatens to reverse these gains, underscoring the urgent need to embed adaptability into policy frameworks beyond times of crisis [61, 62, 63, 64]. To ensure equitable outcomes, regulatory systems must evolve in tandem with technological innovation. Encouragingly, several jurisdictions are already implementing agile approaches: the U.S. Interstate Medical Licensure Compact facilitates cross-state physician practice, the United Kingdom’s regulatory sandbox model supports policy-aligned innovation, Australia’s National Digital Health Strategy standardizes telemedicine reimbursement across states, and India’s eSanjeevani platform have streamlined remote consultations through national data and credentialing standards. These examples show that flexible, inclusive policymaking [61, 62, 63, 64].
The TEF is a structured, multi-dimensional tool designed to address and reduce digital health disparities that persist despite rapid advancements in AI-driven telemedicine [65, 66, 67]. While technological innovation has progressed swiftly, access and health outcomes remain uneven—particularly in underserved communities. Unlike vague calls for equity, the TEF offers a unified model that breaks digital readiness into four measurable pillars: technology readiness, infrastructure access, policy environment, and community capacity [61, 62, 63, 64, 65, 66, 67].
These pillars collectively assess the technological maturity, accessibility, regulatory support, and community-centered elements necessary for equitable digital health care. The framework produces an equity readiness score (ERS), allowing policymakers, providers, and technologists to pinpoint gaps and prioritize interventions. Case studies—such as a low-income urban clinic in the United States and a rural telemedicine initiative in Latin America demonstrate the TEF’s ability to uncover context-specific strengths and challenges, from digital literacy and device access to infrastructural and regulatory barriers. By offering a scalable, equity-centered assessment model, the TEF equips stakeholders with actionable insights to implement inclusive, effective telemedicine solutions ensuring digital transformation advances health equity rather than deepening existing inequalities [61, 62, 63, 64, 65, 66, 67].
As shown in Table 4 AI-driven telemedicine has the potential to transform healthcare delivery in underserved communities, but without deliberate policy interventions and a strong commitment to equity, the digital divide will continue to widen [67]. Equitable access depends on policies that fund rural broadband expansion and support multilingual platforms to ensure connectivity and culturally competent care for non-English-speaking populations [68]. Building trust within regional communities is also essential; this requires digital literacy initiatives to address concerns about privacy, bias, and relevance, as well as co-developing telemedicine solutions that reflect community needs [42, 43, 44]. Ensuring transparency and accountability in AI systems—through clear data governance, explainable algorithms, and ongoing audits—can further mitigate bias and strengthen public confidence [44]. Finally, cross-sector collaboration among governments, tech companies, NGOs, and academic institutions is vital for developing sustainable and inclusive digital health systems by leveraging shared expertise, resources, and evidence-based practices [45, 46, 47].
AI-driven telemedicine holds immense potential to transform health care, but this must be guided by equity, inclusion, and justice. Innovation alone is not enough; digital health must be rooted in a rights-based approach that ensures universal access and addresses structural barriers like the digital divide, affordability, and low health literacy. To achieve global health equity goals such as Universal Health Coverage (UHC) and Sustainable Development Goal 3 (SDG 3), AI solutions must be community-centered designed with, not just for, underserved populations and tailored to cultural and regional contexts. Collaboration across governments, tech developers, healthcare providers, NGOs, and academia is essential to build sustainable, inclusive systems. Ultimately, bridging the digital divide requires integrating innovation with lived experience, so AI-powered telemedicine becomes not just a tool of progress, but a foundation for health equity worldwide.
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Written By
Mthabisi Talent George Moyo
Submitted: 01 May 2025Reviewed: 08 May 2025Published: 08 July 2025
Open access peer-reviewed chapter - ONLINE FIRST
