ArticleJournal of medical Internet research2022
Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Audiologist-Guided Multimodal AI for Pure-Tone Audiometry and Tympanometry Interpretation and Reporting.Journal of medical systems · 2026Article
- Artificial Intelligence and Machine Learning in Audiology and Hearing Disorders: A Scoping Review with Bibliometric and Thematic Mapping (1995-2025).Audiology research · 2026Review
- Hearing health professionals' attitudes and perceived skills toward artificial intelligence.BMC medical education · 2026Article
- Value of an automated machine learning model with post-hoc explanation for predicting healthcare-seeking delays among residents in Tibetan regions.Frontiers in public health · 2026Article
- Does ChatGPT update itself? Accuracy of ChatGPT in tympanostomy tube guidance: A comparative analysis with current literature.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Performance and reliability evaluation of an improved machine learning-based pure-tone audiometry with automated masking.World journal of otorhinolaryngology - head and neck surgery · 2025Article
- Behavior recognition technology based on deep learning used in pediatric behavioral audiometry.Scientific reports · 2025Article
- Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.Orphanet journal of rare diseases · 2025Review
- Validating Smartphone-Based and Web-Based Applications for Remote Hearing Assessment.Journal of the American Academy of Audiology · 2025Article
- Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025Article
- Developing a diagnostic support system for audiogram interpretation using deep learning-based object detection.Journal of otology · 2025Article
- Article
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- Understanding, experience and attitudes towards artificial intelligence technologies for clinical decision support in hearing health: a mixed-methods survey of healthcare professionals in the UK.The Journal of laryngology and otology · 2024Article
- Changing Knowledge, Principles, and Technology in Contemporary Clinical Audiological Practice: A Narrative Review.Journal of clinical medicine · 2024Review
- An Initial Validation of Community-Based Air-Conduction Audiometry in Adults With Simulated Hearing Impairment Using a New Web App, DigiBel: Validation Study.JMIR formative research · 2024Article
- Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study.Frontiers in neuroscience · 2024Article
- Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions.Frontiers in artificial intelligence · 2024Article
- The Hearing Test App for Android Devices: Distinctive Features of Pure-Tone Audiometry Performed on Mobile Devices.Medical devices (Auckland, N.Z.) · 2024Review
- Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S.BioData mining · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals.
objectiveThis study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review.
methodsA search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to identify relevant reports from the peer-reviewed literature. Key information about each report's scope and details was collected to assess the commonalities among the approaches.
resultsA total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results.
conclusionsIn the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.