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Peer-reviewedCC BY 4.0Journal of Clinical Medicine

Hearing Smarter: A Scoping Review of Artificial Intelligence Implementation in Hearing Devices

  • Cameron Hanegan1,2
  • Aya Andos1,3
  • Rahul Ranganath4
  • Ashley Ezeldin4
  • Hunter Price4
  • Jordan Ford4
  • Andrew Mangan1
  • Gresham Richter1,5
5 affiliations
  1. 1Department of Otolaryngology-Head and Neck Surgery, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
  2. 2College of Medicine, University of Central Florida, Orlando, FL 32827, USA
  3. 3School of Osteopathic Medicine, A.T. Still University in Arizona, Mesa, AZ 85206, USA
  4. 4College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
  5. 5Department of Pediatric Otolaryngology, Arkansas Children’s Hospital, Little Rock, AR 72202, USA
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Abstract

Background/Objectives: Hearing loss affects over 1.57 billion people worldwide, and many who could benefit from hearing aids do not wear them. Poor speech understanding in noise is one of the main complaints, and several artificial intelligence (AI) models have been proposed to address it. This review aims to characterize AI applications in hearing devices and offers a framework for advancing from model development to clinical application. Methods: Following PRISMA-ScR guidelines, PubMed/MEDLINE, Embase, CINAHL, Web of Science, and APA PsycInfo were searched for peer-reviewed original studies published from 2010 to 2025. Data regarding AI task, architecture, input, training method, validation, and latency were collected for each model. Results: In total, 46 studies met the inclusion criteria. Speech enhancement was the most common task (58.7%), and deep neural networks were the most common architecture (34.8%). Training was predominantly offline (93.5%) and supervised (91.3%). Validation relied mainly on database/simulation (71.7%) and normal-hearing listeners (39.1%); only 1 model (2.2%) was validated using hearing-impaired listeners in real-world conditions. Ten models (21.7%) met the latency requirements for hearing aid use. Conclusions: AI has been applied across many hearing device tasks, but most models remain early in development. Clinical translation will require on-device implementation and real-world validation in hearing-impaired listeners.

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Cite

BibTeXRIS
BibTeX
@article{hanegan2026hearing,
  title = {{Hearing Smarter: A Scoping Review of Artificial Intelligence Implementation in Hearing Devices}},
  author = {Cameron Hanegan and Aya Andos and Rahul Ranganath and Ashley Ezeldin and Hunter Price and Jordan Ford and Andrew Mangan and Gresham Richter},
  journal = {Journal of Clinical Medicine},
  year = {2026},
  month = oct,
  doi = {10.3390/jcm15208124},
  url = {https://www.mdpi.com/2077-0383/15/20/8124},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Hearing Smarter: A Scoping Review of Artificial Intelligence Implementation in Hearing Devices
AU  - Cameron Hanegan
AU  - Aya Andos
AU  - Rahul Ranganath
AU  - Ashley Ezeldin
AU  - Hunter Price
AU  - Jordan Ford
AU  - Andrew Mangan
AU  - Gresham Richter
PY  - 2026
DA  - 2026/10/01
JO  - Journal of Clinical Medicine
DO  - 10.3390/jcm15208124
UR  - https://www.mdpi.com/2077-0383/15/20/8124
AB  - Background/Objectives: Hearing loss affects over 1.57 billion people worldwide, and many who could benefit from hearing aids do not wear them. Poor speech understanding in noise is one of the main complaints, and several artificial intelligence (AI) models have been proposed to address it. This review aims to characterize AI applications in hearing devices and offers a framework for advancing from model development to clinical application. Methods: Following PRISMA-ScR guidelines, PubMed/MEDLINE, Embase, CINAHL, Web of Science, and APA PsycInfo were searched for peer-reviewed original studies published from 2010 to 2025. Data regarding AI task, architecture, input, training method, validation, and latency were collected for each model. Results: In total, 46 studies met the inclusion criteria. Speech enhancement was the most common task (58.7%), and deep neural networks were the most common architecture (34.8%). Training was predominantly offline (93.5%) and supervised (91.3%). Validation relied mainly on database/simulation (71.7%) and normal-hearing listeners (39.1%); only 1 model (2.2%) was validated using hearing-impaired listeners in real-world conditions. Ten models (21.7%) met the latency requirements for hearing aid use. Conclusions: AI has been applied across many hearing device tasks, but most models remain early in development. Clinical translation will require on-device implementation and real-world validation in hearing-impaired listeners.
SN  - 2077-0383
ER  -