Artificial Intelligence, also known as AI, has been on the rise for decades.
A prominent example would be Bombe from World War II. Bombe helped decipher codes and secret messages being sent by Germans, which would later be known for the allied success in World War II. How would a device that was used in 1939 for a World War relate to audiology in 2025? Two simple words, pattern recognition.
A big part of AI is machine learning. This is a subfield of AI to help identify patterns and make predictions. All machine learning focuses on is pattern recognition, whereas generative AI is a tool that can be used to create new subject matter. As we know, all different kinds of advances, whether that is AI or machine learning, have spread rapidly throughout the world. This includes health and human services, which in turn includes audiology.
The most prominent example of AI regarding audiology would be hearing aids. Machine learning started to get integrated into audiology through hearing aids in the early to mid-2000’s. Different manufacturers began to program their devices to learn the users gain preferences. Many new hearing aids have machine learning features to differentiate sounds. They can also calibrate to focus only on the noises it is anticipating the user to hear in certain scenarios. Aural rehabilitation for speech-language training has had machine learning introduced to it as well.
Now, Bombe during World War II was not AI- it only had machine learning capabilities through pattern recognition. Bombe was not a computer, but a machine used to break the Enigma code. A smaller model of a different version of Bombe, also known as The Turing Machine. The Turing Machine explored which issues can be solved by an algorithm. The inventor of both Bombe and the Turing Machine, Alan Turing, believed that instead of being explicitly programmed for every task, a machine should be able to learn like a small child does. He suggested using logistical machines and predicted that machines could mature like how humans do into adulthood with enough storage and learning over time. Both Bombe and The Turing Machine laid the foundation for modern computers, and the idea that the concept of a general machine could carry out any type of computations. Alan Turing’s research from 1950 outlined a fifty-year-long program about artificial intelligence and described the concept of “thinking machines”.
I highly doubt that Mr. Turing had any fraction of the thought that part of his inventions would be used down the line to help people all around the world hear better. However, he did have the timeline predicted for the “thinking machines” spot-on. But on the opposite side of the coin, there are some more questionable AI and machine learning developments that can also negatively impact audiology. Besides hearing aids integrating AI, we are starting to see the idea that hearing evaluations can be taken over by AI as well. While AI could be beneficial during hearing exams, it cannot replace the hands-on nature or quality of a provider. A provider would end up having to re-evaluate the work the AI test computed and that would only happen if the algorithms and data were set correctly. The range of artificial intelligence’s capacity in the hearing evaluations are concerning most audiologists. There have been some talk of at home testing for hearing once AI is better developed in this department. Dr. Gerhart from The Hearing Review says, “I would not want to see AI replacing the diagnostician and the treatment of hearing loss, because hearing loss is not something that can be self-identified. It’s really hard to get a proper diagnosis, and degree and severity of the loss, without having a test that’s performed by a licensed hearing healthcare professional”.
The use of artificial intelligence as a tool rather than a replacement is the best option for patients and healthcare providers. Dr. Gerhart also states that he fears that consumers may turn to those sources before they turn to a professional and he wouldn’t want them to replace the expertise of a hearing healthcare professional with AI. He continues to say, “I think there is a fundamental component of quality hearing healthcare, and I think that’s the human touch, being able to have one-on-one interaction with a hearing care professional where we can guide patients through how to overcome the challenges that they’re experiencing, which I don’t think AI can deliver.” At the end of the day, quality care is what is most important. Loved ones do not need to walk into an office or clinic and worry about whether or not they will be taken care of.
Machine learning and artificial intelligence cannot recreate compassion and empathy like humans. While the incorporation of these technologies have been steady over the past twenty years, our attention to detail and levels of service have also increased at each Center for Hearing Care location. Health and human services will always try to evolve to create a process that is quick and easy for the patient and provider. At the Centers for Hearing Care, we do not strive to just be quick; we give white-glove service that every patient deserves. AI might be interesting, but it is not as important as the quality of care we give. So, what is the future for audiology with AI in it? There is no exact answer; we just know our care will always be the same as it has been
Brynna Snyder
Patient Care Coordinator