Content area

Abstract

Background: Hearing loss results from diverse biological insults along the auditory pathway, including sensory hair cell death, neural degeneration, and central auditory processing deficits. Implantable auditory neuroprostheses, such as cochlear and brainstem implants, aim to restore hearing by directly stimulating neural structures. Advances in neurobiology and device technology underpin the development of more sophisticated implants tailored to the biological complexity of auditory dysfunction. Aim: This narrative review of reviews aims to map the integration of artificial intelligence (AI) in auditory neuroprosthetics, analyzing recent research trends, key thematic areas, and the opportunities and challenges of AI-enhanced devices. By synthesizing biological and computational perspectives, it seeks to guide future interdisciplinary efforts toward more adaptive and biologically informed hearing restoration solutions. Methods: This narrative review analyzed recent literature reviews from PubMed and Scopus (last 5 years), focusing on AI integration with auditory neuroprosthetics and related biological processes. Emphasis was placed on studies linking AI innovations to neural plasticity and device–nerve interactions, excluding purely computational works. The ANDJ (a standard narrative review checklist) checklist guided a transparent, rigorous narrative approach suited to this interdisciplinary, rapidly evolving field. Results and discussion: Eighteen recent review articles were analyzed, highlighting significant advancements in the integration of artificial intelligence with auditory neuroprosthetics, particularly cochlear implants. Established areas include predictive modeling, biologically inspired signal processing, and AI-assisted surgical planning, while emerging fields such as multisensory augmentation and remote care remain underexplored. Key limitations involve fragmented biological datasets, lack of standardized biomarkers, and regulatory challenges related to algorithm transparency and clinical application. This review emphasizes the urgent need for AI frameworks that deeply integrate biological and clinical insights, expanding focus beyond cochlear implants to other neuroprosthetic devices. To complement this overview, a targeted analysis of recent cutting-edge studies was also conducted, starting from the emerging gaps to capture the latest technological and biological innovations shaping the field. These findings guide future research toward more biologically meaningful, ethical, and clinically impactful solutions. Conclusions: This narrative review highlights progress in integrating AI with auditory neuroprosthetics, emphasizing the importance of biological foundations and interdisciplinary approaches. It also recognizes ongoing challenges such as data limitations and the need for clear ethical frameworks. Collaboration across fields is vital to foster innovation and improve patient care.

Details

1009240
Business indexing term
Title
Bridging Neurobiology and Artificial Intelligence: A Narrative Review of Reviews on Advances in Cochlear and Auditory Neuroprostheses for Hearing Restoration
Publication title
Biology; Basel
Volume
14
Issue
9
First page
1309
Number of pages
31
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20797737
Source type
Scholarly Journal
Language of publication
English
Document type
Literature Review
Publication history
 
 
Online publication date
2025-09-22
Milestone dates
2025-07-24 (Received); 2025-09-16 (Accepted)
Publication history
 
 
   First posting date
22 Sep 2025
ProQuest document ID
3254471700
Document URL
https://www.proquest.com/scholarly-journals/bridging-neurobiology-artificial-intelligence/docview/3254471700/se-2?accountid=208611
Copyright
© 2025 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2026-01-19
Database
ProQuest One Academic