The Rise of AI Chatbots in Hearing Healthcare : The Hearing Journal

Applications of AI

One of the most exciting recent innovations is the deployment of artificial intelligence (AI) chatbots based on large scale language models (LLMs). AI chatbots are a type of generative AI that can generate text. Other examples of generative AI create images (such as DALL-E or Stable Diffusion, see Figures 1 and 2) or music (such as Jukebox). In November 2022, Open AI published his ChatGPT. It is an AI chatbot that can respond to questions (so-called prompts) from users, participate in conversations, and generate responses to user questions (i.e. prompts) that are almost indistinguishable from human questions. The launch of ChatGPT represents a technological revolution that could change the face of healthcare as we know it, including hearing healthcare. ChatGPT is not an isolated example, but part of a global race to see who can develop the most compelling AI chatbot. Besides Microsoft-funded Open AI, big companies such as Meta, Google, and Tencent have launched similar proprietary products based on LLMs (such as LaMDA).

Figure 1:

Artwork by DALL E. Prompt: “Hearing healthcare, the rise of AI chatbots in the digital arts.” ChatGPT, AI, artificial intelligence, hearing healthcare, chatbots.

Figure 2:

Artwork by DALL E. Prompt: “A futuristic illustration of ‘The Planet of AI Chatbots in Hearing Healthcare’, a movie about ear invasions that are also hospitals.” ChatGPT, AI, artificial intelligence, hearing healthcare, chatbots.

table 1:

An AI chatbot for listening to medical applications, risks, and research priorities for patients, clinicians, and researchers.

AI chatbots and healthcare

AI chatbots are computer programs that use natural language processing (NLP) to communicate with humans. They are trained on a large collection of languages ​​(e.g., all written books and most of the Internet) to predict what responses are most likely to various queries. increase. To a human user, it may appear as though the system can understand your questions and provide personalized advice, recommendations, and support. In reality, chatbots don’t understand the world around them, nor the human body and its health. Still, the potential applications of AI chatbots in healthcare are broad, with use cases such as patients, clinicians, researchers, and trainees. 1

A general trend for the use of AI chatbots in healthcare is to increase accessibility (to medical knowledge) and affordability of care. Chatbots could provide his 24/7 access to healthcare advice and support, reduce the need for face-to-face consultations, and improve patient outcomes. Additionally, AI chatbots have the potential to provide valuable insights and data to medical professionals, allowing them to make more informed decisions about patient care. Greater transparency into the data these chatbots access and use to generate their output is important and has been raised as a concern for existing systems. 2

Hearing healthcare can use chatbots to support patients, clinicians, and researchers (Table 1).

Patients and AI chatbots

Patients can benefit from AI chatbots in listening to their healthcare in a number of ways. One potential application is initial screening and intervention recommendations. For example, a patient can ask about their symptoms and hearing history and interact with a chatbot that provides symptom self-management, further evaluation, or treatment recommendations based on the patient’s response. 3 This may be the case if the patient is unsure whether they are experiencing hearing loss or is reluctant to seek medical attention, or if severe hearing loss prevents them from communicating with the clinician. is especially useful for Chatbots also serve as screening tools for comorbidities, including educational resources, self-management tools, and social needs. Four Patients can receive hearing health information, prevention tips, and advice on how to manage their hearing condition. Chatbots can provide information on using management options such as hearing aids, how to change batteries, and troubleshooting common issues. However, a potential risk is that chatbots may not provide accurate recommendations, which can lead to delayed diagnosis and inappropriate treatment.

Clinicians and AI chatbots

Clinicians can benefit from AI chatbots in listening to healthcare in a number of ways. Chatbots can assist in data collection and analysis by collecting data on a patient’s hearing health, including self-reported symptoms and hearing aid usage. Chatbots provide summary reports or visualizations to help clinicians make treatment decisions, such as providing summary reports of patient hearing test results, highlighting areas of concern, and providing recommendations for further evaluation or treatment. can provide Another potential application is to aid decision-making and treatment planning. For medical applications, Google and Deep Mind developed Med-Palm. This is his LLM incorporating clinical knowledge evaluated using newly developed benchmarks. Five A chatbot that unlocks clinical knowledge can suggest treatment options based on a patient’s hearing health history and symptoms, and provide information on the benefits and risks of each option. For example, a chatbot can suggest certain types of treatment based on a patient’s hearing test results and preferences. Chatbots can also help clinicians communicate in a more accessible and person-centric way.

A potential risk is that chatbots may not provide the same level of clinical judgment and decision-making as human health professionals. It may be biased or outdated, leading to misdiagnosis and inappropriate treatment.

Hearing researchers and AI chatbots

Researchers can benefit from AI chatbots in listening to healthcare in a number of ways. Chatbots can collect large amounts of data from different populations, giving researchers valuable insight into the prevalence and impact of hearing loss. For example, a chatbot could collect data on the prevalence of tinnitus in different countries and regions. Another potential application is to facilitate clinical trials and research studies. Chatbots can screen potential participants for eligibility, collect her informed consent, and manage research protocols. Four For example, a chatbot can collect self-reported data on hearing aid use and satisfaction in large clinical trials.

However, a potential risk is that the data collected by chatbots may be incomplete or biased. Especially if the chatbot is only accessible to certain populations, or if the questions asked by the chatbot are not culturally sensitive or suitable for all participants. 2 Additionally, chatbots can inadvertently exclude certain populations from research studies, such as individuals who do not have access to or are not comfortable using technology.

current priorities

Investigating the (clinical) application of AI chatbots in hearing healthcare is an urgent priority. In this rapidly changing landscape, general guidelines have been developed for the appropriate use of AI chatbots by researchers. Academic journals widely agree that chatbots may not co-author research papers. 2,6 For hearing research applications, evaluating the effectiveness and reliability of chatbots in collecting and analyzing hearing health data should be a priority. Researchers and clinicians need to ensure that chatbots can provide accurate recommendations and treatment options, and that the data collected by chatbots can be trusted.

Usability is another important research priority to ensure that chatbots are user-friendly and accessible to as many patients as possible, regardless of age or technical literacy. Cultural sensitivity is also important to ensure that your chatbot is culturally sensitive and suitable for everyone. There are also important ethical considerations for using chatbots in hearing healthcare, including issues related to informed consent, data privacy, and data security. Researchers also need to evaluate the long-term consequences of using chatbots for hearing healthcare. This includes evaluating the impact of chatbots on patient outcomes such as quality of life, satisfaction, and adherence to treatment. Overall, research priorities for AI chatbots in auditory research should focus on ensuring that chatbots are accurate, reliable, accessible and culturally sensitive.

Guidelines on the proper use of AI chatbots by clinicians or patients are not yet available. Language models are primarily trained using texts from the internet, so they may have the same popular opinions, stereotypes, and prejudices that exist on the internet. For this reason, experts and patient groups should test which prompts yield the best results and provide guidelines to avoid misuse and misunderstanding.


The rise of AI chatbots (based on LLM) represents a significant technological advancement that has the potential to revolutionize hearing healthcare. AI chatbots have the potential to provide personalized advice and support to patients, while providing valuable insights and data to medical professionals. However, given the potential risks and benefits of AI chatbots, it is important to prioritize further research to ensure that these technologies are used ethically, effectively, and safely in hearing healthcare.


We would like to thank ChatGPT, an AI chatbot trained by Open AI using Large Language Models (LLM), for providing valuable insight and guidance for this article. To experiment with rapid engineering and get a first impression of what AI chatbots such as ChatGPT can and cannot provide, we spoke with ChatGPT in patient and clinician roles.


Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT at the 2023 USMLE: Potential for AI-Assisted Medical Education Using Large Language Models PLOS Digit Health 2

van Dis EA, Bollen J, Zuidema W, van Rooij R, Bockting CL 2023 ChatGPT: Five priorities for research Nature 614 224 226

Wasmann J, Swanepoel DW AI Modern Frontiers Part 2: AI Chatbots and Clinical Audiology [Internet] Computational Auditory Network Feb 2023 [cited 2023 6 Mar]Available from .

Kocielnik R, Agapie E, Argyle A, et al. 2019 HarborBot: Chatbot for Social Needs Screening AMIA Annual Symposium Proceedings 552 561

Singhal K, Azizi S, Tu T, et al. 2022 Taken from a large language model arXiv 1 44 that encodes clinical knowledge

Stokel-Walker C 2023 ChatGPT listed as author of research paper Nature 613 620 621

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