Artificial Intelligence in Hearing Aids Technology: Meta-Analysis & How to Share the Patient His Needs Depending upon His Life Style
Modern hearing aid technology continues to evolve through artificial intelligence (AI) innovation which delivers superior capabilities for speech clarity while reducing noise and tailoring listening adjustments. This study analyzes the effectiveness of AI-assisted methods starting with machine learning and audio signal processing when enhancing hearing aid operations. A research team analyzed ten experimental and comparative studies concerning this subject which were published between 2018 and 2024. The development of hearing aids utilizes machine learning techniques in 30% of all cases alongside signal processing methods at 25% according to research findings. The applications of artificial intelligence in hearing aids led to substantial enhancements of speech clarity alongside noise reduction functionality and product acceptance satisfaction level by 45% and 38% and 50% respectively. The application of adaptive sound processing as well as pattern recognition technologies appears less prevalent in available research therefore requiring more scientific attention. The efficiency of hearing aids relies heavily on two important factors: device response speed at 41.6% and computational power at 33.3%. This study emphasizes the transformative role of AI in hearing aids, particularly in personalizing user experiences and optimizing performance in dynamic auditory environments. Future research should focus on improving adaptive processing capabilities, integrating AI with real-time health monitoring, and addressing challenges related to energy efficiency and affordability. By refining AI applications, hearing aid technology can further enhance the quality of life for individuals with hearing impairments.
Authors
- Qusai Maarouf Ismail
Publication Details
- Journal
- Open Journal of Applied Sciences
- Published
- 2025-01-01
- DOI
- https://doi.org/10.4236/ojapps.2025.157134
- Citations
- 2
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 2.72