Impact Of AI Towards Improving Hearing Conditions Of Impaired Individuals Case Study

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Introduction of Impact Of AI Towards Improving Hearing Conditions Of Impaired Individuals Case Study

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The advancement that is present in artificial intelligence is responsible for making changes in different fields such as retail as well as has been known to be making a positive impact on hearing as well. However, the reliability of hearing healthcare is based on a particular model known as the labor-intensity service model, which is counted to be a drawback in the particular case. Apart from this, the other sites that are present are majority do not have accessibility, lack computation tools for making research on hearing. Furthermore, the hearing process is itself a complicated process as it is directly linked with the health of humans; therefore, experiments are not done much on the following topic. AI is an important invention of modern science which provides advantage for people of all ages, gender, community an as well as dwelling in worldwide.

1. Key Problems

There is the presence of a huge amount of data related to the hearing of an individual, irrespective of this fact, and there is the presence of multiple issues. One of the noteworthy problematic areas is treatment as well as diagnosis of hearing disorders (Nature, 2022). The nature of the disorder is multi-factorial, which is noted to be arising with time and is therefore of the primary issues. In addition to this, the understanding is limited in the case of mechanistic underpinning and thus is counted to be a problematic area. The treatment of the inappropriate condition of the middle ear is not done properly, along with issues in automated audiogram measurements. The hearing devices are found to not fit properly in the ears of the patients as they are not developed by taking the measurements of the ears as they vary accordingly.

2. AI Solution

Automated services are provided as an Artificial Intelligence (AI) solution for the individuals who are facing issues in the hearing. As per the view of McInnes (2022), the automated services are provided by the staff that is highly trained in technology such that they can guide the persons facing hearing issues. The respective staffs are well aware of the use of specialized equipment, which allows them to guide the people who would be using it such that they can use it by themselves. The implementation of AI in the hearing devices provides the advantage of providing sign language such that in case the individual is deaf, it allows them to understand the words that are spoken by others. In consequence, these auditory functions are mimicked as it is responsible for improvising the hearing services working capability.

AI Solution for Hearing

(Source: Case Study)

3. Impact

The advancement of AI is known to be highly efficient in transforming the hearing field such that people facing issues can listen to it. According to the view of Bhowmik et al. (2021), in previous times, the machines of hearing are known to be achieving advancements such as natural language processing as well as Automatic Speech Reorganization (ASR). The impact of AI in medicine is positive, which includes technologies related to eye screening depending on deep neural networks (DNN). In the case of hearing, the potentiality of AI is high as for the machines of hearing that the latest developed consist of AI technology in order to match with both auditory systems functionality as well as main components of the structure. The capability of AI is high in case of mitigating the issues that are needed to be mitigated in the hearing field along with meeting the needs in healthcare of hearing. There is a presence of disentanglement among the links that are established by perceptual impairments as well as pathologies which are appropriately assisted by AI.

 AI in Hearing Device

(Source: Case Study)

4. Outcomes and Critical Appraisal

In hearing healthcare in recent times makes use of models as it is responsible for improving the lifestyle of billions of initials which are present in different nations such as the UK on early basis. According to the view of Zhang et al.(2021) declared that the scenario is applicable for adults yet not for children as they have to wait for a long period of time to use AI hearing devices. Trails are made for the children facing issues related to the middle ear, and they have to wait for a long, long time as the research is disrupted and yet to be done. On the other hand, Wei et al. (2020) depicted the fact that trials, as well as error methods, are used as well for individuals having tinnitus, and there is a high tendency of not to gain high benefits in the particular field. Irrespective of implementing AI in hearing devices, the deaf personalities are not able to gain the advantage of understanding that speech is the form of noise as well as enjoy the tune of music.

The people who are known to be living in LMICs are considered to be the luckiest ones as they do not need any kind of further treatment or gave to wait for further research. Although Miyakawa et al. (2019) stated that AI is expected and found to be making huge improvements which are dramatic in nature in hearing treatment, there is an impact of AI that is substantial. Hearing healthcare is reshaped by the application of AI and the opportunities gained from it. Apart from AI in the hearing devices, AI is used for treatment and diagnosis purposes as well. In the digital era, the developers responsible for AI development are presently seen to be working on converting present technology into robust implementation. In addition to this, the clinicians of the hearing department as well as the hearing scientists are working hard to ensure the important facts such that the suitable data related to training are available. In contrast to this, Guo et al. (2020) expressed that there is a high necessity for the presence of data on clinical infrastructure in order to provide clear support for rapid adoption. It is not possible to make a complete transformation in hearing healthcare with the help of AI.


Bhowmik, A.K., Fabry, D.A., Armour, P., Berghel, H., Charette, R.N. and King, J.L., (2021). Hear, Now, and in the Future: Transforming Hearing Aids Into Multipurpose Devices. Computer54(11), pp.108-120.

Fountaine, T., McCarthy, B. and Saleh, T., (2019). Building the AI-powered organization. Harvard Business Review97(4), pp.62-73.

Guo, A., Kamar, E., Vaughan, J.W., Wallach, H. and Morris, M.R., (2020). Toward fairness in AI for people with disabilities SBG@ a research roadmap. ACM SIGACCESS Accessibility and Computing, (125), pp.1-1.

McInnes, E., (2022). Bystander Attitudes to Hearing Family Violence: An Australian Survey. International Journal of Criminology and Sociology11, pp.48-54.

Miyakawa, A., Wang, W., Cho, S.J., Li, D., Yang, S. and Bao, S., (2019). Tinnitus correlates with downregulation of cortical glutamate decarboxylase 65 expression but not auditory cortical map reorganization. Journal of Neuroscience39(50), pp.9989-10001.

Nature, (2022), Harnessing the power of artificial intelligence to transform hearing healthcare and research Available at: [Accessed on: 19th April, 2022]

Wei, Y., Zhou, J., Wang, Y., Liu, Y., Liu, Q., Luo, J., Wang, C., Ren, F. and Huang, L., (2020). A review of algorithm & hardware design for AI-based biomedical applications. IEEE transactions on biomedical circuits and systems14(2), pp.145-163.

Zhang, S., Dong, Y., Qiang, R., Zhang, Y., Zhang, X., Chen, Y., Jiang, P., Ma, X., Wu, L., Ai, J. and Gao, X., (2021). Characterization of Strip1 expression in mouse cochlear hair cells. Frontiers in Genetics12, p.488.

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