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

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