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Audiology Research
Special Issues
Emerging Technologies in Audiology: Advancing Assessment, Intervention, and Accessibility
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Emerging Technologies in Audiology: Advancing Assessment, Intervention, and Accessibility

A special issue of Audiology Research (ISSN 2039-4349). This special issue belongs to the section "Hearing".

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 1469

Special Issue Editors

Dr. Antonino Maniaci

E-Mail Website
Guest Editor
Department of Medicine and Surgery, University of Enna Kore, 94100 Enna, Italy
Interests: audiology; otology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Lentini

E-Mail Website
Guest Editor
Department of Medicine and Surgery, University of Enna Kore, 94100 Enna, Italy
Interests: audiology; otology; artificial intelligence

Special Issue Information

Dear Colleagues,

Recent technological advances have drastically reshaped the field of audiology. From artificial intelligence-powered hearing aids to remote hearing healthcare delivery, emerging technologies are changing how we assess, treat, and support persons with hearing impairments. This Special Issue will explore the leading edge of technological innovation in audiological care, encompassing but not limited to AI applications, teleaudiology, virtual/augmented reality in rehabilitation, new diagnostic tools, and innovative hearing device technologies. We seek high-quality original research, systematic reviews, and perspective papers that critically reflect on the opportunities and challenges of translating such innovations into clinical service.

Dr. Antonino Maniaci
Dr. Mario Lentini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Audiology Research is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in audiology
  • teleaudiology
  • digital hearing aids
  • virtual reality rehabilitation
  • remote hearing care
  • machine learning diagnostics
  • smart hearing technologies
  • digital health solutions

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Published Papers (2 papers)

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Research

14 pages, 1437 KB  
Article
Increased Listening Effort: Is Hearing Training a Solution?—Results of a Pilot Study on Individualized Computer-Based Auditory Training in Subjects Not (Yet) Fitted with Hearing Aids
by Dominik Péus, Jan-Patric Schmid, Andreas Koj, Andreas Radeloff and Michael Schulte
Audiol. Res. 2025, 15(5), 124; https://doi.org/10.3390/audiolres15050124 - 27 Sep 2025
Abstract
Background: Hearing and cognition decline with age. Hearing is now considered an independent risk factor for later cognitive impairment. Computerized cognitive auditory training is being discussed as a possible adjunctive therapy approach. Objectives: The aim of this exploratory study is to investigate [...] Read more.
Background: Hearing and cognition decline with age. Hearing is now considered an independent risk factor for later cognitive impairment. Computerized cognitive auditory training is being discussed as a possible adjunctive therapy approach. Objectives: The aim of this exploratory study is to investigate how the success of a computer-based cognitive auditory training (CCAT) can be measured. For this purpose, the influence of a CCAT on different dimensions of hearing and cognition was determined. Materials and Methods: 23 subjects between 52 and 77 years old were recruited with normacusis to moderate hearing loss. They underwent 40 digital training lessons at home. Before, during, and after completion, concentration ability with the d2-R, memory (VLMT), subjective hearing impairment (HHI), hearing quality (SSQ12), listening effort in noise (ACALES), and speech understanding in noise (GÖSA) were measured. Results and Discussion: In this uncontrolled, non-randomized study, one of the main findings was that cognitive dimensions, namely processing speed, improved by 12.11 ± 16.40 points (p = 0.006), and concentration performance improved by 12.56 ± 13.50 points (p = 0.001), which were not directly trained in CCAT. Learning performance also improved slightly by 4.00 ± 7.00 (p = 0.019). Subjective hearing handicap significantly reduced by 10.70 ± 12.38 (p = 0.001). There were no significant changes in the SSQ-12 (p = 0.979). Hearing effort improved by 1.79 ± 2.13 dB SPL (p = 0.001), 1.75 ± 2.09 (p = 0.001), and 3.32 ± 3.27 dB (p < 0.001), respectively. Speech understanding in noise did not improve significantly. CCAT is likely to improve several dimensions of hearing and cognition. Controlled future studies are needed to investigate its efficacy. Full article
(This article belongs to the Special Issue Emerging Technologies in Audiology: Advancing Assessment, Intervention, and Accessibility)
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16 pages, 824 KB  
Article
ChatGPT and Microsoft Copilot for Cochlear Implant Side Selection: A Preliminary Study
by Daniele Portelli, Sabrina Loteta, Mariangela D’Angelo, Cosimo Galletti, Leonard Freni, Rocco Bruno, Francesco Ciodaro, Angela Alibrandi and Giuseppe Alberti
Audiol. Res. 2025, 15(4), 100; https://doi.org/10.3390/audiolres15040100 - 6 Aug 2025
Cited by 1 | Viewed by 698
Abstract
Background/Objectives: Artificial Intelligence (AI) is increasingly being applied in otolaryngology, including cochlear implants (CIs). This study evaluates the accuracy and completeness of ChatGPT-4 and Microsoft Copilot in determining the appropriate implantation side based on audiological and radiological data, as well as the [...] Read more.
Background/Objectives: Artificial Intelligence (AI) is increasingly being applied in otolaryngology, including cochlear implants (CIs). This study evaluates the accuracy and completeness of ChatGPT-4 and Microsoft Copilot in determining the appropriate implantation side based on audiological and radiological data, as well as the presence of tinnitus. Methods: Data from 22 CI patients (11 males, 11 females; 12 right-sided, 10 left-sided implants) were used to query both AI models. Each patient’s audiometric thresholds, hearing aid benefit, tinnitus presence, and radiological findings were provided. The AI-generated responses were compared to the clinician-chosen sides. Accuracy and completeness were scored by two independent reviewers. Results: ChatGPT had a 50% concordance rate for right-side implantation and a 70% concordance rate for left-side implantation, while Microsoft Copilot achieved 75% and 90%, respectively. Chi-square tests showed significant associations between AI-suggested and clinician-chosen sides for both AI (p < 0.05). ChatGPT outperformed Microsoft Copilot in identifying radiological alterations (60% vs. 40%) and tinnitus presence (77.8% vs. 66.7%). Cronbach’s alpha was >0.70 only for ChatGPT accuracy, indicating better agreement between reviewers. Conclusions: Both AI models showed significant alignment with clinician decisions. Microsoft Copilot was more accurate in implantation side selection, while ChatGPT better recognized radiological alterations and tinnitus. These results highlight AI’s potential as a clinical decision support tool in CI candidacy, although further research is needed to refine its application in complex cases. Full article
(This article belongs to the Special Issue Emerging Technologies in Audiology: Advancing Assessment, Intervention, and Accessibility)
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