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. 2026 Sep 15;9(9):e2633867. doi: 10.1001/jamanetworkopen.2026.33867

Inclusive Digital Health Strategies for Persons With Disabilities

A Scoping Review

Emre Umucu 1,2,3,✉, Beatrice Lee 4, Andrew Vernon 5, Mustafa Rfat 6, Kaloyan Kamenov 7, Mélanie Gréaux 7
PMCID: PMC13579439  PMID: 42742940

This scoping review evaluates evidence on disability inclusion and health equity in digital health technologies.

Key Points

Question

Does existing worldwide evidence adequately address disability inclusion in digital health technologies, and what strategies promote equitable access for persons with disabilities?

Findings

In this scoping review of 40 peer-reviewed articles and 40 articles from gray literature, 5 themes were identified: factors enabling access (accessibility by design, participatory codesign with persons with disabilities, and representative datasets), stakeholder roles, government-led initiatives, contextual barriers, and emerging technologies. Participatory codesign and accessible design principles were consistently identified as enablers of equitable digital health.

Meaning

The findings suggest achieving digital health equity for persons with disabilities requires accessibility by design from inception, meaningful codesign partnerships, representative datasets, and sustained governmental commitment.

Abstract

Importance

Digital health technologies, including artificial intelligence (AI), have transformed health care delivery and access to health care worldwide. However, persons with disabilities, including those with injuries and traumas, remain disproportionately excluded from these innovations.

Objective

To synthesize worldwide evidence on disability inclusion and health equity in digital health technologies.

Evidence Review

This scoping review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews and synthesized literature published between 2019 and 2025. Searches were conducted in MEDLINE and Web of Science, supplemented by gray literature searches. Only English-language sources that focused on digital health, pertained to persons with disabilities, and addressed health equity were included. Searches were last conducted in December 2024.

Findings

Of 925 records identified, 836 were screened, 137 underwent full-text review, and 40 peer-reviewed articles were included, supplemented by 40 gray literature sources for a total of 80 included documents. Findings were structured around 5 themes: (1) factors enabling equitable access to digital health, such as accessibility by design, participatory codesign, and representative datasets; (2) key stakeholders, including persons with disabilities, caregivers, technology developers, researchers, and policymakers; (3) government-led initiatives across World Health Organization regions; (4) contextual factors, including cultural, social, and economic influences; and (5) emerging innovations, such as AI-powered assistive technologies and inclusive digital platforms. Participatory codesign and accessible design principles were consistently identified as enablers of equitable digital health.

Conclusions and Relevance

The findings of this scoping review suggest that achieving health equity requires researchers to prioritize disability-specific metrics, conduct rigorous evaluations, and engage in participatory research with persons with disabilities. Governments should adopt international accessibility standards, developers should engage end users as codesigners, health systems should train clinicians in inclusive digital health, and civil society should advocate for inclusion and monitor compliance.

Introduction

As of 2022, it was estimated that approximately 1.3 billion individuals worldwide had a significant disability.1 This prevalence is projected to rise considerably due to the increasing global burden of noncommunicable diseases, aging, and longer life expectancy made possible by advanced health technologies,2,3,4,5 including artificial intelligence (AI) tools integrated into health care systems, transforming how individuals access care.1 However, persons with disabilities, especially those living in low- and middle-income countries (LMICs) or in rural or remote areas, continue to face major challenges in accessing digital health care services.1,6,7,8 Socioecological barriers compound this exclusion, limiting the potential of digital health to improve equitable outcomes for persons with disabilities.6,7,8

Although AI has significantly advanced digital health care services, disability inclusion remains inadequately integrated in digital health care, resulting in persistent digital exclusion for persons with disabilities.9,10,11,12 Systemic barriers within and beyond health care systems deepen the digital divide, disproportionately disadvantaging persons with disabilities in terms of accessing and benefiting from digital health care.13,14 In response, there have been international calls to improve digital health technologies as accessible tools that meet the diverse needs of persons with disabilities,1 since research highlights the importance of inclusive design as the basis of equitable digital health technology for these individuals.6,12,15,16,17

Despite worldwide commitments to advancing health equity for persons with disabilities, there is limited research on how disability inclusion in digital health is conceptualized and implemented. Therefore, this scoping review aimed to synthesize existing evidence on the sociocultural, economic, and policy factors influencing digital health inclusion and the innovations, interventions, and implementation strategies used to enhance the reach, quality, and equity of digital health services for persons with disabilities.

Methods

Study Design, Protocol, and Selection

This scoping review examined the inclusivity of digital health technologies in alignment with the Global Report.1 Given the rapidly evolving nature of digital health and AI for persons with disabilities, a 2-stream, multisource search strategy was implemented following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews.18 The first stream was a systematic search of MEDLINE, via Ovid, and Web of Science using keywords and synonyms compiled from Medical Subject Headings,19 last conducted in December 2024 (eAppendix 1 in Supplement 1). The second stream was an iterative gray literature search, expanded beyond the initial period to capture contemporaneous research, policy, strategies, and guidance as an equally essential component of the evidence base. Literature from 2019 to 2025 was included to capture advances in AI-enabled technologies, COVID-19 pandemic–driven shifts in remote health care, and accelerated international efforts on disability inclusion. We conceptualized digital health technologies as digital systems, tools, and applications that leverage existing technologies to promote health, prevent diseases and disabilities, and provide early intervention and rehabilitation, consistent with the World Health Organization (WHO) classification of digital health interventions. A wide range of publication types was reviewed, including peer-reviewed qualitative, quantitative, and mixed-methods studies; reviews; and reports or policies. We made additional efforts to identify literature from LMICs. Studies and documents were eligible if they (1) focused on digital health, (2) included or pertained to persons with disabilities or addressed disability inclusion, (3) addressed health equity and inclusion, (4) were published between 2019 and 2025, and (5) were available in English.

Data Extraction and Synthesis

Data extraction was performed independently by 2 reviewers (E.U., B.L.), with a third (A.V.) resolving discrepancies and leading the gray literature search. Findings were synthesized thematically using an iterative inductive approach. One reviewer (E.U.) screened all records, while a second (B.L) assessed a random subsample (n = 83) for interrater reliability; agreement was moderate (κ, 0.68; 95% CI, 0.62-0.74; P < .001).20 Because digital health and AI for persons with disabilities are rapidly evolving, the search was updated iteratively through 2025.

Results

Following PRISMA scoping review guidelines, 925 records were initially identified through database searches: 581 from MEDLINE via Ovid and 344 from Web of Science. After removing 89 duplicates, 836 unique records were retained for title and abstract screening. Of these, 699 were excluded owing to an irrelevant outcome or population, leaving 137 for full-text review; a further 97 were excluded for irrelevant digital technology or scope, yielding 40 peer-reviewed studies. These were combined with 40 documents from the gray literature search for a final corpus of 80 documents (Figure 1).9,12,15,16,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95 Geographic scope was assessed for the 69 documents (86.3%) for which setting information was available (11 documents [13.8%] did not report a country or region and were excluded from geographic analyses). Of these 69 documents, 28 (40.6%) were global in scope. The United States was the most common single-country setting (14 [20.3%]), followed by the United Kingdom (2 [2.9%]). By WHO region, documents were concentrated in the Region of the Americas (19 [27.5%]) and the European Region (16 [23.2%]), with fewer from the Western Pacific Region (4 [5.8%]), the African Region (3 [4.3%]), and the South-East Asia Region (1 [1.4%]).

Figure 1. PRISMA Diagram.

Flowchart of literature screening and inclusion counts with two search pathways. Light gray flowchart boxes with dark text connected by gray arrows. Upper left box: 925 Records identified, with two indented source lines, 581 MEDLINE via Ovid and 344 Web of Science. A rightward arrow leads to a smaller box reading 89 Duplicates removed. A downward arrow from the upper left box leads to a larger box reading 836 Screened by title and abstract by multiple reviewers. From this box, a rightward arrow leads to a box reading 699 Excluded due to irrelevant outcome or population. A downward arrow from the 836 box leads to a box reading 137 Full-text records retrieved and assessed for eligibility. From the 137 box, a rightward arrow leads to a box reading 97 Excluded due to irrelevant digital technology or scope. A downward arrow from the 137 box leads to a box reading 40 Included. A downward arrow from the 40 Included box leads to a final lower left box reading 80 Records included in review. On the right side, a separate upper right box reads 100 Records screened via ongoing gray literature search. From this box, a rightward arrow leads to a box reading 60 Excluded due to irrelevant topic. A downward arrow from the 100 box leads to a box reading 40 Included. A connector line from the right-side 40 Included box runs downward and then leftward, joining into the final lower left box reading 80 Records included in review.

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Factors Promoting or Hindering Health Equity and Disability Inclusion in Digital Health

AI and Emerging Smart Technologies

Advanced technologies, including AI, robotics, 3-dimensional printing, digital health twins, and blockchain-based smart systems, are increasingly applied to digital health devices and services across health conditions,21,22,23,24,25,26,96,97 including efforts to address disability-related functional limitations. Evidence on AI and smart health technologies for persons with disabilities indicates that inclusive design frameworks embedding accessibility features are critical for usability across impairment types15 and that user-centered interfaces developed through codesign with persons with disabilities are essential enablers of equitable access.15,16,27 Representative, bias-free datasets are likewise crucial for mitigating bias and preventing inequities in AI health tools.15,27 Regarding barriers, El Morr et al27 found that most AI research for persons with disabilities was medically oriented, reflecting traditional medical models that prioritize symptom management over psychosocial and socioecological dimensions98 and thereby limiting inclusive, equitable solutions. Models trained on unrepresentative datasets or omitting disability-specific variables may further increase the risk of biased predictions and of excluding persons with disabilities from AI benefits.12,15,28 In addition, data privacy, confidentiality, and security remain substantial barriers to advancing disability inclusion through AI health tools.15,27,28,29,30,99,100,101

Assistive Technology for Health, Communication, and Participation

Although assistive technology (AT) can improve assessment and psychosocial and physical health,31,32,33,102,103,104,105,106,107 the availability of well-designed tools remains uneven; a review of mobile applications for auditory training in children with hearing impairments, for example, found few applications that meaningfully supported language development.34 More broadly, AT integration into health, communication, and participation is shaped by enabling and limiting factors that may directly influence the inclusion of persons with disabilities in digital health.35 On the enabling side, virtual reality (VR) applications for persons with spina bifida showed potential to improve mental health and socialization in this population,36 and a low-cost, home-based VR program offered adolescents with physical disabilities more accessible exercise opportunities.37 Everyday assistive products also support participation: access to priority products on the WHO assistive products list enabled persons with disabilities to take part in sports and community activities.38 On the limiting side, products with traditional aesthetics and symbols associated with visual impairment were perceived negatively, often linked to stigma.39 Environmental barriers within the home may further constrain equitable use; 1 study found that modifications and assistive devices were more likely to be implemented in higher-income regions, reinforcing socioeconomic disparities.40 Clinician-level challenges were associated with further reduction in the adoption and equitable implementation of AT for persons with disabilities.41

Digital Health Interventions and Remote Care

Digital health interventions and high-quality online health information can serve as integral parts of care for disability by reducing treatment gaps, supporting health promotion, and improving engagement in health domains,42,43,44,45,108,109,110,111,112,113,114,115,116,117,118 thereby advancing health equity, although barriers remain. Involving children with long-term conditions and their parents throughout design and evaluation makes such tools more usable, acceptable, and effective,46 and building in customizability and adaptability further supports usability and equal access.47,119 Accessibility is central: 1 study found that people with intellectual impairments and those with communication, language, and/or calculation impairments reported the lowest use of and greatest difficulty with eHealth services,48 and another reported that equitable telemedicine access for people with hearing loss required both clear clinician communication and patient self-advocacy supported by accessibility tools.49 Sustaining adoption and adherence over the long term, in turn, requires balancing the social, organizational, and technical dimensions of these tools.50

Inclusive, Ethical, Safety, and Policy Dimensions in Disability Technology

Multiple studies emphasized accessibility features built in from the outset, often through codesign, to make digital tools inclusive and usable for persons with disabilities.51,52,53 A website prototype using easy-to-read language and the Web Content Accessibility Guidelines,120 for example, was found to be a feasible way to deliver accessible online health information to adults with intellectual disability (ID),54 whereas design limitations such as unclear audio can undermine usability.55,56,57 Persons with disabilities may be underrepresented in the large datasets that drive technology development and resource allocation through AI, risking the development of future tools that are less relevant for this group.58 Ethics matter as well. Jabin et al26 noted that despite the potential of digital health twins to improve care, their implementation raised unresolved challenges around ethics and design. Eriksson and Ineland59 found that although digitalizing services for persons with ID may enable participation, structures for supporting people online remained incomplete, leaving unresolved questions about legal frameworks. Smythe et al60 argued that AT for children with disabilities is underfinanced and excluded from health-system planning, with unmet need greater in LMICs. At the policy level, van Kessel et al61 described a digital health paradox in which the same technologies that expand access can also deepen inequalities for persons with disabilities, arguing from cross-national policy analysis that health equity must be designed into digital health systems.

Key Stakeholders and Their Roles in Promoting Inclusive Digital Health

Persons With Disabilities and Families or Caregivers

Recognizing persons with disabilities and their caregivers as primary stakeholders and codesigners is essential for reducing barriers to inclusive digital health technology.51,62,63,64,121,122,123,124 Persons with disabilities have expressed strong interest in codesigning digital health tools,65,66 yet these tools are often designed without consideration of their needs and perspectives.67 Participatory design models that engage persons with disabilities as cocreators can produce technologies that are more user centered and better aligned with needs in everyday settings.68,125 Caregivers, in turn, provide substantial support to persons with disabilities in using digital health technologies,1 although digital health can inadvertently shift responsibilities from health professionals onto caregivers, requiring them to interpret complex health information beyond traditional caregiving roles.47,64

International Organizations and Technology Developers

International organizations set universal norms and provide technical guidance on inclusive digital health strategies to improve health equity outcomes for persons with disabilities, including by facilitating capacity-building initiatives that strengthen health systems.64 The International Disability Alliance, for example, developed a practical guide to support organizations of persons with disabilities when actively engaging in health system strengthening processes.126 Technology developers, for their part, are advised to integrate universal design principles to ensure that tools are accessible to persons with disabilities17,127 and to involve persons with disabilities actively throughout the design and evaluation process to address their unique needs and preferences.69 Failure to do so risks reinforcing or exacerbating existing health inequities.

Researchers and Health Care Practitioners

Given that research on the feasibility of digital health for persons with disabilities and chronic conditions remains sparse,70,71 researchers are called on to conduct needs assessments, codesign solutions with end users, evaluate health equity impacts, and disseminate findings to inform practice and policy.47,50 Newman-Griffis and colleagues128 identified 3 priority areas in which research can drive equity in digital health systems: (1) leveraging natural language processing (NLP) to analyze free-text clinical data related to functional status, (2) developing innovative NLP methods to capture contextual and environmental factors, and (3) integrating patient-reported narratives into clinical and research data. Health care practitioners are likewise key stakeholders in advancing health equity for persons with disabilities through the effective use of inclusive digital technologies.64 Persistent systemic barriers, such as bias and lack of broadband access, delay inclusive digital health use72; however, training can help practitioners use digital and AI-based interventions appropriately.67 Importantly, health care organizations deploying AI and algorithmic tools should disclose their use, evaluate these tools for bias, collect disability-specific data, and include persons with disabilities in ethics reviews.73

Governments, Regulations, and Policymakers

Regulations and policies, including government funding and efforts to strengthen the accessibility of health information technology, can advance inclusive digital health for persons with disabilities.69,74 However, worldwide evidence from a recent scoping review indicated that only 22.9% of included studies examined leadership and governance in the context of disability inclusion in health systems.75 Although this figure reflects the broader literature rather than the articles included in the present review, it highlights a significant gap in governmental and managerial engagement.

Government Efforts to Advance Disability-Inclusive Digital Health

Multiple government efforts to advance disability-inclusive digital health are ongoing.76,77,78,129,130 The US Access Board signed a memorandum of understanding with the American Association of People With Disabilities and the Center for Democracy & Technology to ensure that AI development advances accessibility and protects the rights of persons with disabilities.77 In Europe, strategies were outlined to support member states in building inclusive, person-centered systems.78,99,129 In the Asia-Pacific region, the Australian Digital Health Agency, along with People With Disability Australia, is enhancing the accessibility and inclusivity of national health platforms,79 and the South Korea Ministry of Health and Welfare launched the Healthcare 4.0 era, prioritizing health equity for older adults and persons with disabilities and making commitments to expand technologies that improve access to high-quality care.80 Across Africa, the African Union’s Agenda 2063 outlines commitments to leveraging technology for persons with disabilities, with nations adopting WHO guidance to improve digital health access.81,82,131

Contextual Factors Shaping Equity and Inclusion in Digital Health

Digital health technologies are embedded in sociocultural systems, so equitable adoption requires aligning design with the socioeconomic realities of the communities.83,132 First, a lack of culturally inclusive design can reduce engagement with and trust in digital health interventions,84 and negative attitudes toward disability and assistive products may reduce uptake, particularly when devices are perceived as stigmatizing or poorly fitted.85 Second, technologies reflecting ableist assumptions can reproduce social barriers. Developing health technologies to fix disability may diminish adoption by persons with disabilities, restricting equitable participation, reinforcing systemic barriers, and alienating the communities they are meant to serve128; these dynamics are compounded by existing mistrust, particularly toward AI-driven care.86,133 Even where accessibility legislation exists, persons with disabilities may be unable to locate accessibility information, and many seek web- and mobile-based tools to help them find this information.87 Third, persons with disabilities are at increased risk of poverty, encountering financial barriers such as high out-of-pocket costs.64,85 Several studies examined digital health for persons with disabilities in LMICs88,89; 1 study found that in these settings, mHealth interventions could function without reliable internet and should offer instructional audio in multiple languages in places where more than 1 language is official.90

Emerging Technologies Supporting Inclusive and Equitable Digital Health Services

Collaborative efforts among stakeholders are already producing innovative solutions for inclusive digital health. The Speech Accessibility Project, for example, is building inclusive speech datasets to improve AI speech recognition for people with speech disabilities.91 The Artificial Intelligence of Things applied to AT, including emerging technologies such as smart glasses, shows considerable promise, although significant development gaps persist.92 The Friendly Robot to Ease Dementia, an affordable, chatbot-powered robot, provides medication reminders and interactive storytelling for people with dementia.93 AI-driven prostheses, such as the LUKE Arm and the Össur Proprio Foot, illustrate how machine learning can anticipate user movements and even provide sensory feedback, enhancing usability.94 In addition, an AI-based app developed through codesign aims to make complex health correspondence easier to understand for people with learning disabilities or autism by simplifying jargon, describing images, and supporting communication through multimodal audio, video, and speech recognition tools.95,134

Discussion

Digital health and AI tools can improve health equity for persons with disabilities, yet exclusion persists across the technology development life cycle from design to implementation, with consistent enablers (factors facilitating equitable access)15,16 or barriers.27,28 Figure 2 situates these findings within a socioecological model of digital health equity and disability inclusion, in which enablers and barriers operate simultaneously across 5 nested levels, including the person with a disability, interpersonal networks, organizations and health systems, government and regulation, and the wider societal and contextual environment. Digital health technologies are cross-cutting rather than level-specific factors, functioning as enablers or barriers depending on how they are designed, governed, and deployed. Inclusive design and codesign are foundational: applying participatory codesign improves the usability of digital health for persons with disabilities121,122 and can shift clinician-centric tools toward autonomy-supporting technologies aligned with users’ lives. Representative, disability-aware datasets are essential; when disability is absent from training data, AI models fail to account for the needs and social determinants of persons with disabilities, producing biased outputs.17,28 Large language modeling may capture functioning, contextual factors, and narratives from persons with disabilities, though this requires sustained investment.

Figure 2. Socioecological Model of Digital Health Equity and Disability Inclusion.

Nested socioecological framework listing enablers and barriers to digital inclusion. Five nested, color-banded rectangles with bold headings and paired lines labeled Enablers and Barriers. At the top, on a light gray background, a heading reads Social and contextual factors: Sociocultural systems, socioeconomic realities, language, and L M I C settings. Beneath it, a line labeled Enablers lists cultural inclusivity, multilingual design, trust building; a line labeled Barriers lists stigma and ableism, poverty and cost, digital divide, A I mistrust. Below, a larger peach rectangle contains a heading Government, policy, and regulation: Policy development, infrastructure, funding and investment, and worldwide approach. Under it, Enablers lists funding, accessibility legislation, disability-inclusive governance; Barriers lists weak governance, policy and legal gaps, lack of infrastructure. Inside the peach area, a light blue rectangle contains a heading Organizations and health systems: Technology developers, international organizations (eg, W H O), clinicians, and researchers. Under it, Enablers lists universal design, representative data, clinician training; Barriers lists medical-model focus, algorithmic bias, dataset gaps, lack of training. Inside the blue area, a white rectangle contains a heading Interpersonal factors: Families, caregivers, clinicians, and peer codesigners. Under it, Enablers lists codesign, caregiver support, clear communication, participatory approaches; Barriers lists caregiver burden, clinician communication gaps, digital divide, lack of training, self-stigma. Inside the white area, a light green rectangle contains a heading Person with disabilities: Sensory, mobility, cognitive, communication, emotional, physical, and developmental factors. Under it, Enablers lists accessible and customizable tools, digital literacy; Barriers lists impairment-specific access, low eHealth use. Centered near the bottom of the green rectangle, bold text reads Outcome: Health equity and digital inclusion.

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There are cross-cutting areas. For example, digital health technologies operate at every level, either as enablers (factors facilitating equitable access) or barriers. AI indicates artificial intelligence; LMIC, low- and middle-income country; WHO, World Health Organization.

Privacy and accountability remain central concerns: ethical implementation demands transparency and active involvement of disability stakeholders in oversight.30,101,129 It also requires robust infrastructure; inadequate broadband continues to constrain telehealth, particularly in rural areas.1,64,135,136 Without systematic investment, digital health will remain inaccessible, unsustainable, and inequitable. In addition, several studies have reported implementation failures: codesign can be resource intensive, accessible design features may be added as afterthoughts rather than integrated from inception, and the scalability of innovations remains constrained by high costs, limited maintenance infrastructure, and inadequate training, particularly in LMICs.27,28,98

Socioeconomic barriers and stigma may reduce adoption and sustain mistrust, including skepticism toward AI-enabled care among persons with disabilities, caregivers, and clinicians.39,137 Economic challenges are especially critical, since households of individuals with disabilities already face higher costs and lower incomes than households of individuals without disabilities,1,64 while underdeveloped infrastructure in low- and middle-income areas adds a further layer of exclusion.23,24 Thus, health equity through inclusive digital health depends on individual, social, and economic realities.

Several initiatives show how governance accelerates disability inclusion. The European Union Artificial Intelligence Act prohibits manipulative and discriminatory practices, requires accessible notifications, and encourages codes of conduct that include disability stakeholders78,129; the WHO–International Telecommunication Union global standard for telehealth accessibility provides concrete criteria for inclusive service design and has enabled early implementation of services such as OpenTeleRehab135,138; and national funding for connectivity and digital inclusion, such as broadband and device-access programs, directly addresses infrastructure barriers to telehealth for persons with disabilities.77,136 However, our findings highlight a critical gap: the lack of systematic monitoring and evaluation of these innovations’ impact on health equity. Without rigorous assessment, it is difficult to tell whether digital health solutions advance equity or inadvertently widen disparities. Incorporating disability-disaggregated data, user-reported outcomes, and long-term equity indicators rather than technical performance metrics alone is essential to ensure that progress translates into measurable gains in accessibility and inclusion.

Disability-inclusive design should begin at the planning phase, enabling early integration of universal design principles, multimodal interactions, and compatibility with mainstream health platforms,51,138 which reduces retrofit costs, improves quality and satisfaction, and enhances outcomes for persons with disabilities. Formal codesign guidelines should become standard practice, meaningfully involving persons with disabilities in needs assessment, prototyping, and testing while supporting evaluation of accessibility and health outcomes. Adherence to universal design and accessibility standards, usability feedback from diverse groups with disabilities across socioeconomic and geographic contexts, and attention to health equity impacts are essential for implementation. eAppendix 2 in Supplement 1 lists our recommendations for stakeholders.

Limitations

This study has limitations. The review relied on literature published between 2019 and 2025, potentially excluding earlier research. The gray literature search was challenging due to the rapidly evolving nature of the AI and digital health field, with new AI initiatives and disability-focused programs emerging continuously. Consequently, some relevant documents may have been missed. Furthermore, variations in reporting quality and standards across gray literature sources may limit comparability. Word limits precluded summarizing all findings. Much of the indexed evidence originated from high-income countries despite targeted attempts to include LMIC sources, and only English-language sources were included, a particular concern given the worldwide diversity of disability experiences and digital health innovation. As this was a scoping review, no formal quality appraisal was conducted; because the review included policy documents, organizational reports, and gray literature alongside peer-reviewed studies, sources varied considerably in rigor, and conclusions should be interpreted with awareness that some evidence may reflect organizational or advocacy positions rather than empirical findings. Future reviews should consider formal quality assessments to better differentiate evidence strength.

Conclusions

The findings of this scoping review suggest that achieving health equity in inclusive digital health technology requires researchers to prioritize disability-specific metrics, conduct rigorous evaluations, and engage in participatory research with persons with disabilities. Governments should adopt international accessibility standards, developers should engage end users as codesigners, health systems should train clinicians in inclusive digital health, and civil society should advocate for inclusion and monitor compliance.

Supplement 1.

eAppendix 1. Search Strategy

eAppendix 2. Recommendations

jamanetwopen-e2633867-s001.pdf (274.9KB, pdf)
Supplement 2.

Data Sharing Statement

jamanetwopen-e2633867-s002.pdf (14.1KB, pdf)

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Associated Data

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Supplementary Materials

Supplement 1.

eAppendix 1. Search Strategy

eAppendix 2. Recommendations

jamanetwopen-e2633867-s001.pdf (274.9KB, pdf)
Supplement 2.

Data Sharing Statement

jamanetwopen-e2633867-s002.pdf (14.1KB, pdf)

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