What question did this study set out to answer?

The review aims to assess the evolution of AI and ML in audiology and hearing disorders, analyzing its thematic development and clinical readiness.

February 26, 2026Open Access

Artificial Intelligence and Machine Learning in Audiology and Hearing Disorders: A Scoping Review with Bibliometric and Thematic Mapping (1995–2025)

Key Points

The review aims to assess the evolution of AI and ML in audiology and hearing disorders, analyzing its thematic development and clinical readiness.
Conducted a scoping review using the Web of Science Core Collection.
Included original and review articles from 1995 to 2025.
Employed bibliometric and thematic mapping to analyze trends and collaboration networks.
Categorized research into clinical domains based on translational maturity.
Analyzed 127 publications showing marked research growth after 2020.
Noted a doubling time of approximately 2.1 years for publication output.
Identified China, the United States, and South Korea as the top contributors.
Thematic analyses highlighted a shift towards AI-driven methodologies, especially in machine learning and cochlear implants.
Most studies were at proof-of-concept stages with limited external validation.

Abstract

Background and Objectives: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into audiology, supporting diagnosis, screening, rehabilitation, and digital health. Despite rapid growth, the literature remains methodologically and clinically heterogeneous, limiting a consolidated view of research trajectories and translational readiness. This scoping review examined the evolution of AI and ML applications in audiology and hearing disorders, focusing on thematic development, research productivity, collaboration patterns, and clinical orientation. Methods: A scoping review was conducted using the Web of Science Core Collection (Science Citation Index Expanded). Original and review articles published between 1995 and 2025 were included. Bibliometric and thematic mapping were applied to analyze publication trends, citation patterns, keyword evolution, and collaboration networks. A structured translational categorization assessed clinical domains and validation maturity. Findings reflect the Web of Science-indexed segment of the literature. Results: A total of 127 publications were analyzed. Research output increased markedly after 2020, with an estimated doubling time of approximately 2.1 years. China, the United States, and South Korea contributed the highest publication volumes, although citation impact did not consistently parallel productivity. Thematic analyses revealed a shift toward AI-driven methodological frameworks, particularly in machine learning, deep learning, and cochlear implant-related applications. Most studies remain at proof-of-concept or internally validated stages, with limited external validation. Emerging areas include tele-audiology and personalized hearing aid optimization. Conclusions: AI and ML research in audiology is increasingly application-oriented; however, broader external validation and prospective implementation are required to support routine clinical integration.

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Cite This Study

Ceren Aksoy Koçak (Tue,) studied this question.

synapsesocial.com/papers/699fe38b95ddcd3a253e78b2 https://doi.org/https://doi.org/10.3390/audiolres16020029

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