Tele-Audiology and Artificial Intelligence for Hearing Care in Underserved Populations: A Narrative Review

Tele-Audiology and Artificial Intelligence for Hearing Care in Underserved Populations: A Narrative Review | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 February 2026 V1 Latest version Share on Tele-Audiology and Artificial Intelligence for Hearing Care in Underserved Populations: A Narrative Review Authors : Ali Bahrami , Negar Kazemi , Mohammad Mahdi Khanasir , and Mohammad Shahmansouri 0009-0004-6972-125X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177190572.20674767/v1 227 views 87 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Limited access to hearing healthcare in underserved regions is driven by workforce shortages, inadequate infrastructure, and high costs. Tele-audiology has emerged as a viable approach to improve access, while recent advances in artificial intelligence (AI) have expanded its diagnostic and rehabilitative capabilities. Objective: This narrative review aimed to summarize current evidence on the effectiveness, applications, and challenges of tele-audiology and AI in hearing healthcare delivery. Methods: Peer-reviewed studies published between 2018 and 2025 were identified through searches of PubMed, Web of Science, Scopus, and Google Scholar. Eligible studies addressed tele-audiology or AI-based applications in hearing screening, diagnosis, rehabilitation, or follow-up. A qualitative narrative synthesis was conducted. Results: Findings indicate that tele-audiology can provide clinical outcomes comparable to in-person services while improving accessibility and reducing travel-related costs. AI-based tools demonstrated acceptable diagnostic accuracy in several applications, though performance varied across populations. User satisfaction was generally neutral to positive, with higher acceptance for screening and follow-up services. Conclusion: Tele-audiology, particularly when combined with AI, represents an effective and scalable complement to conventional hearing care. Addressing technical, regulatory, and educational barriers is essential for sustainable implementation. Key words: Tele-audiology, Artificial intelligence, Underserved areas, Telemedicine Introduction Access to hearing services is notably limited in underserved regions. Over 80% of individuals experiencing hearing loss reside in low \RL and middle-income countries, where a significant proportion lack adequate diagnostic and rehabilitation services(1). Some of the most relevant access barriers in these regions include a shortage of audiologists, lack of appropriate hearing health infrastructure, and unequal dissemination between urban and rural areas(2). Moreover, high costs of hearing aids and rehabilitation services, disadvantages in insurance coverage, and low public awareness of the importance of early diagnosis and intervention lead to delays or non-availability of hearing services(3). Studies have indicated that the integration of hearing care into primary health care, along with community-based approaches, has the potential to reduce inequalities in access(4). Furthermore, the utilization of technologies such as tele-audiology as a cost-effective alternative increases the possibility of providing hearing services in remote and underserved areas(5). Tele audiology refers to the delivery of hearing care services via communication technologies and includes hearing screening, diagnostic assessment, therapeutic intervention, and rehabilitation. This approach has received widespread attention, particularly during the COVID-19 pandemic, as an effective way to overcome the limitations of access to hearing services. Evidence suggests that tele-audiology can reduce costs by eliminating the need for travel, increase patient adherence to treatment, and provide reliable clinical outcomes similar to the in-person approach(6). These benefits are particularly important for populations living in remote or underserved areas(5). Several studies have confirmed the clinical feasibility of tele-audiology. Internet-based audiometry systems have shown high compatibility with in-person methods and have enabled simultaneous and remote hearing assessments, even without the physical presence of an audiologist \RL (7).Tele-audiology applications range from screening and diagnostic tests, such as audiometry, ear acoustic emission (OAE), and auditory brainstem response (ABR), to therapeutic interventions, such as hearing aid fitting and verification, tinnitus counseling, and online platform-based therapies(5). Despite these advantages, challenges exist, such as technical difficulties, quality and security of the Internet connection, environmental noise control, and legal restrictions related to professional licensing and reimbursement policies(6). Furthermore, the attitude and acceptance of technology by patients and audiologists play an important role in the successful implementation of tele audiology and require systematic evidence-based reviews(8). Overall, given the dispersion and heterogeneity of the available evidence regarding effectiveness, application scope, and implementation challenges, a comprehensive narrative synthesis is needed to integrate and interpret current findings within a coherent conceptual framework(5). As an emerging technology, artificial intelligence plays a complementary role in the development of tele-audiology and improves the quality of hearing care by improving remote assessment, diagnosis, and monitoring(9, 10). AI-based tools can automate and optimize hearing screening, diagnosis, and intervention processes and increase the accessibility and efficiency of tele audiology services(11). Advances in artificial intelligence are expected to help overcome existing challenges and pave the way for the expansion of high-quality and flexible hearing care (12). Together, tele-audiology and AI present novel avenues for reengineering the provision of hearing care for underserved populations. Their ability to enhance flexibility, efficiency, and scalability may thus inhibit the growth of gaps in access, creating a novel course for the future of hearing care(13, 14). Methods: Study Design This study was conducted as a narrative review to provide an overview of recent developments in tele-audiology and the application of artificial intelligence in hearing healthcare delivery. The narrative approach was selected to allow flexible integration of evidence from diverse study designs and to highlight technological, clinical, and implementation-related aspects relevant to digital health systems. Literature Search A comprehensive literature search was performed in PubMed, Web of Science, Scopus, and Google Scholar to identify relevant studies published between 2018 and 2025. The search strategy combined keywords related to tele-audiology and digital health technologies, including tele-audiology, telemedicine, remote hearing care, artificial intelligence, machine learning, and hearing healthcare. Boolean operators were applied to refine search results. In addition, reference lists of selected articles were screened to capture further relevant publications. Study Selection Studies were included if they addressed the use of tele-audiology technologies in hearing screening, diagnosis, intervention, rehabilitation, or follow-up, and if they reported technological, clinical, or implementation-related outcomes. Quantitative, qualitative, and mixed-methods studies were eligible, including cross-sectional, cohort, and experimental designs. Only peer-reviewed articles published in English were considered. Editorials, opinion papers, conference abstracts without full text, and studies not directly related to hearing healthcare technologies were excluded. Data Extraction and Synthesis Key information was extracted from each included study, including publication year, study design, technological components, type of tele-audiology service, and main findings. Due to methodological heterogeneity across studies, a qualitative narrative synthesis was conducted. The findings were organized thematically, with emphasis on diagnostic performance, technological innovations such as artificial intelligence, user acceptance, infrastructural requirements, and ethical and regulatory considerations relevant to technology-enabled hearing care systems. Results: AI-Based Threshold Estimation Among five included studies evaluating AI-assisted estimation of hearing thresholds, three reported acceptable accuracy within predefined tolerance limits(15). In one study predicting air-conduction and bone-conduction thresholds, deep learning models (LSTM, Bi-LSTM) achieved approximately 60% accuracy within ±5 dB and about 80% within ±10 dB, while tree-based models (Random Forest, XGBoost) showed comparatively higher performance in some analyses (≈72.4% within ±5 dB and 89.5% within ±10 dB)(16). A 2025 study reported high model accuracy in the development dataset but a reduction in performance across external datasets and heterogeneous populations(17). In another study, the wideband automated adaptive testing system yielded significantly lower acoustic reflex thresholds compared with the conventional clinical method(18). Figure 1. Tele-audiology service workflow Figure 2. Artificial intelligence applications in tele-audiology AI Applications in Diagnostic and Vestibular Evaluation Four included studies applied AI-based approaches. In two of these studies (Yoon 2024–2025; Soylemez 2025), the use of AI models was associated with improved diagnostic performance compared with conventional analytical approaches (16, 17). In one 2025 study, AI models predicted posterior canal benign paroxysmal positional vertigo (PC-BPPV) with reported diagnostic accuracy of up to 96.4%(17). In the study by Jendrzejczyk et al., AI models demonstrated stable performance in predicting tinnitus-related outcomes over 3- and 6-month follow-up periods(19). In Kim and Schairer study, an AI-supported approach for estimating air-bone gap thresholds yielded higher accuracy and shorter assessment time than the conventional clinical method(18). Service Accessibility and Cost Outcomes in Tele-Audiology Four included studies reported that tele-audiology interventions were associated with improved accessibility to hearing services and reductions in travel-related and healthcare-associated costs, particularly in geographically remote contexts (20, 21). In a study from remote regions of Australia, tele-audiology implementation was associated with reduced patient travel requirements and increased service uptake, although challenges such as limited internet connectivity and shortages of trained personnel were noted (22). In South Africa, remote hearing-screening programs were reported as feasible and were associated with reductions in geographic barriers and earlier detection of hearing problems(23). Another study indicated that tele-audiology reduced healthcare expenditures and improved service access among underserved groups, while emphasizing the need for adequate technical infrastructure and user training(24)\RL. User and Provider Satisfaction and Acceptance Across the included studies, users and service providers generally reported neutral-to-positive experiences with tele-audiology services, with patients frequently citing improved accessibility and reductions in travel time and costs. In a comparative study of web-based versus in-person services, more than 90% of participants reported satisfaction with web-based service delivery(25). Several studies described initial user concerns or hesitancy toward tele-audiology; however, attitudes were generally more favorable following exposure or service use (20). More favorable attitudes were reported particularly for audiological consultation, hearing-aid follow-up, and hearing screening, whereas confidence levels were lower for more complex diagnostic assessments (26). In a study focused on children and adolescents, tele-audiology services were associated with increased accessibility and reduced care-related costs (27). Studies on remote hearing-aid adjustment reported positive clinician satisfaction (28), and remote follow-up after hearing-aid prescription was reported as comparable to in-person follow-up with respect to outcomes and satisfaction (29). Awareness, Acceptance, and Utilization of Tele-Audiology Services during covid-19 Several studies reported that acceptance and utilization of tele-audiology among patients and hearing-care professionals were relatively limited before the COVID-19 pandemic, but both awareness and use increased during the pandemic (6). In one study examining stakeholders’ perceptions, approximately 55% of patients and 90% of professionals reported awareness of tele-audiology, whereas only 7% of patients had prior experience using it (30). Studies addressing implementation barriers indicated that adoption of tele-audiology is gradual and dependent on infrastructure, technical capacity, and workforce training (31). In a national survey in Australia, although tele-audiology outcomes were reported as positive, consumer use remained low, with many lacking access or receiving limited encouragement from clinic staff (32). Collectively, these studies indicate that the COVID-19 pandemic was associated with increased use of tele-audiology; however, actual uptake and reported satisfaction continue to vary based on user knowledge, access, and preferences for in-person services (33)\RL. Discussion This narrative review synthesizes and critically appraises the evolving evidence on the integration of tele-audiology and artificial intelligence (AI) in hearing healthcare delivery for underserved settings. The collated literature robustly suggests that this convergence represents a transformative paradigm with significant potential to enhance access, efficiency, and scalability of services. Rather than presenting novel primary data, our analysis consolidates existing knowledge to clarify the current state of the field, identify consistent trends and contradictions, and delineate pivotal avenues for future research and implementation. The reviewed evidence consistently affirms the clinical feasibility of tele-audiology across the care continuum, from screening to rehabilitation. A key synthesis emerging from this analysis is that remote pathways appear to function most effectively not as a wholesale replacement for in-person care, but as a strategic complement or extension, particularly in contexts constrained by workforce shortages and geography\RL. Overall, the body of evidence reviewed indicates that artificial intelligence has been favorably assessed as a complementary component of tele-audiology services (15). Nevertheless, some studies report that conventional tree-based models, including random forest and extreme gradient boosting, may outperform artificial intelligence approaches in certain clinical or technical contexts (16). Moreover, the effectiveness of artificial intelligence models appears to be influenced by population-specific characteristics, underscoring the importance of demographic and clinical variability in model performance (17). Despite these constraints, artificial intelligence demonstrates substantial potential in reducing clinical errors through enhanced detection and correction mechanisms (18). In addition, artificial intelligence-based systems may function as supportive tools for patients by enabling the early identification of symptoms such as dizziness or tinnitus, with evidence suggesting stable performance across diverse settings (17, 19)\RL. According to recent studies, tele-audiology can improve access to hearing healthcare services and reduce associated costs (20). These services have also been evaluated as effective and capable of reducing geographical barriers to care delivery (23). However, challenges such as a lack of skilled professionals and the need for stable, high-quality internet infrastructure have been reported (22). The development of appropriate technical and organizational infrastructure is therefore essential to achieve greater efficiency and optimal utilization of this technology (24). Reported experiences with tele-audiology services range from positive to neutral (25). In some applications, such as hearing screening, efforts to build trust and address user concerns have been relatively successful (20)\RL. Nevertheless, confidence levels remain lower for more complex diagnostic procedures (26). Evidence suggests that satisfaction increases when services are delivered through advanced web-based or hybrid models (25). One of the major drivers of the rapid expansion of tele-audiology was the coronavirus pandemic, which substantially increased its adoption(6). Additionally, specialists’ understanding and perception of tele-audiology play a significant role in its implementation (26). Some studies report that despite positive evaluations of tele-audiology, actual utilization remains low due to limited knowledge or lack of trust among professionals (32). Accordingly, improving awareness and education among both specialists and the general public may promote wider and more effective use of tele-audiology services (33). Conclusion: Despite substantial progress demonstrating that tele-audiology can deliver clinical effectiveness comparable to in \RL person services, particularly when complemented by artificial intelligence to enhance accuracy and reduce clinical errors, several barriers continue to limit its optimal efficiency and widespread adoption. Key challenges include the need for stable internet infrastructure, effective management of environmental noise in non-clinical settings, and the development of trust among both clinicians and patients. Moreover, the successful integration of tele-audiology and artificial intelligence requires clear regulatory and ethical frameworks to ensure data protection, confidentiality, and responsible use of automated systems. Insufficient training and limited digital competence among audiologists further constrain effective implementation. Addressing these technical, organizational, and human factors is therefore essential to support the sustainable expansion and broader clinical adoption of tele-audiology\RL. Limitation: This narrative review has several methodological constraints. The qualitative synthesis precludes quantitative meta-analysis, limiting the objective comparison of outcomes across studies. Despite systematic searches, selection bias may be present, and the included evidence varies in methodological rigor. The rapid evolution of the technologies means this synthesis represents a temporal snapshot. Furthermore, findings are primarily focused on underserved settings and may not be generalizable to all contexts, and a formal economic analysis was beyond its scope. These limitations underscore the need for more standardized primary research and systematic reviews in this evolving field. Acknowledgments: The authors extend their sincere appreciation to Mr. Karen Mousavi for his generous assistance and kind encouragement throughout this project. Furthermore, we acknowledge the utilization of the following tools: ChatGPT-5 (OpenAI) for proofreading and grammatical corrections, and Gemini 2.5 Pro (Google) for its role in the creation of Figures 1 and 2. Funding: The authors have nothing to report. Data availability: No underlying data was collected or produced in this study. Conflicts of Interest: The authors declared no potential conflicts of interest for the research, authorship and publication of this article. Authors’ Contributions: Ali Bahrami: conceptualized and designed the study, conducted the literature search, contributed to the interpretation of findings, and wrote the initial draft of the manuscript. Mohammad Shahmansouri: as the corresponding author, contributed to the conceptualization and study design, performed the literature search, and provided critical scientific revision and interpretation of the results. Negar Kazemi: contributed to data extraction, interpretation of findings, and drafting of the manuscript. Mohammad Mahdi Khanasir: contributed to data extraction, interpretation of findings, and drafting of the manuscript. All authors critically reviewed the manuscript, approved the final version, and agree to be accountable for all aspects of the work. References: 1. Organization WH. World report on hearing: World Health Organization; 2021.2. Blazer DG, Domnitz S, Liverman CT, for Adults AHHC, National Academies of Sciences E, Medicine. Hearing health care services: Improving access and quality. Hearing Health Care for Adults: Priorities for Improving Access and Affordability. 2016.3. Waterworth CJ, Marella M, O’Donovan J, Bright T, Dowell R, Bhutta MF. Barriers to access to ear and hearing care services in low-and middle-income countries: a scoping review. Global Public Health. 2022;17(12):3869-93.4. Patterson RH, Suleiman O, Hapunda R, Wilson B, Chadha S, Tucci D. Towards universal access: A review of global efforts in ear and hearing care. Hearing research. 2024;445:108973.5. Swanepoel DW, Hall III JW. A systematic review of telehealth applications in audiology. Telemedicine and e-Health. 2010;16(2):181-200.6. D’Onofrio KL, Zeng F-G. Tele-audiology: Current state and future directions. Frontiers in Digital Health. 2022;3:788103.7. Givens GD, Blanarovich A, Murphy T, Simmons S, Blach D, Elangovan S. Internet-based tele-audiometry system for the assessment of hearing: a pilot study. 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The Journal of Laryngology & Otology. 2025;139(4):329-32.28. Quar TK, Lim YF, Rashid MF, Chu SY, Chong FY. The Influence of Remote Hearing Aid Adjustment Technology on the Current Practice of Tele-Audiology among Audiologists in Malaysia. Journal of the American Academy of Audiology. 2024;35(07/08):204-13.29. Tao KF, Moreira TdC, Jayakody DM, Swanepoel DW, Brennan-Jones CG, Coetzee L, et al. Teleaudiology hearing aid fitting follow-up consultations for adults: single blinded crossover randomised control trial and cohort studies. International journal of audiology. 2021;60(sup1):S49-S60.30. Mui B, Muzaffar J, Chen J, Bidargaddi N, Shekhawat GS. Hearing Health Care Stakeholders’ Perspectives on Teleaudiology Implementation: Lessons Learned During the COVID-19 Pandemic and Pathways Forward. American journal of audiology. 2023;32(3):560-73.31. Mahomed-Asmail F, Coco L, Robler SK. Asynchronous Telepractice in Audiology: Feasibility and Implementation. Perspectives of the ASHA Special Interest Groups. 2025:1-10.32. Kelsall-Foreman I, Bacusmo EAZ, Barr C, Vitkovic J, Campbell E, Coles T, et al. Teleaudiology Services in Australia: A National Survey of Hearing Health Care Consumers Amid the COVID-19 Pandemic. American Journal of Audiology. 2024;33(2):518-31.33. Eikelboom RH, Bennett RJ, Manchaiah V, Parmar B, Beukes E, Rajasingam SL, et al. International survey of audiologists during the COVID-19 pandemic: Use of and attitudes to telehealth. International Journal of Audiology. 2022;61(4):283-92. Graphical Abstract: Integration of tele-audiology and artificial intelligence enables remote hearing assessment, diagnosis, and rehabilitation. This approach improves accessibility, reduces costs, and supports effective management in underserved populations. Key components include AI-assisted threshold estimation, remote consultations, and digital follow-up. Figure 1. Tele-audiology service workflow. The diagram illustrates the main stages of remote hearing care, including Screening, Diagnosis, Intervention, and Follow-Up. Artificial intelligence (AI) supports each stage through threshold estimation, remote analysis, device adjustment, and outcome monitoring, enhancing accessibility and efficiency in underserved populations \RL . Figure 2. Artificial intelligence applications in tele-audiology. The workflow shows how hearing data (e.g., OAE, ABR) are processed by AI models, including LSTM, Bi-LSTM, Random Forest, and XGBoost, to generate threshold estimations, diagnostic outputs, and clinical recommendations, supporting remote assessment and management Information & Authors Information Version history V1 Version 1 24 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords health care learning (artificial intelligence) telemedicine Authors Affiliations Ali Bahrami Ahvaz Jundishapur University of Medical Sciences View all articles by this author Negar Kazemi Ahvaz Jundishapur University of Medical Sciences View all articles by this author Mohammad Mahdi Khanasir Ahvaz Jundishapur University of Medical Sciences View all articles by this author Mohammad Shahmansouri 0009-0004-6972-125X [email protected] Ahvaz Jundishapur University of Medical Sciences View all articles by this author Metrics & Citations Metrics Article Usage 227 views 87 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ali Bahrami, Negar Kazemi, Mohammad Mahdi Khanasir, et al. Tele-Audiology and Artificial Intelligence for Hearing Care in Underserved Populations: A Narrative Review. Authorea . 24 February 2026. 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