LM304 - AudGPT: Understanding and Shaping the Future of Artificial Intelligence in Hearing and Balance Care

Abstract:
Artificial Intelligence (AI) and Machine Learning (ML) have rapidly become household buzzwords. It seems inevitable that these revolutions sweeping other industries will have a significant impact on hearing and balance care. This talk aims to highlight the pivotal role that computational audiology plays in moving forward hearing and balance healthcare, emphasizing the potential of AI and ML in diagnosing, treating, and preventing auditory and vestibular disorders. We will also discuss the ethical considerations surrounding the use of these tools and the ethical and legal importance of doing so in a trustworthy and transparent way.

Summary:
Artificial Intelligence (AI) and Machine Learning (ML) have rapidly become household buzzwords. This talk aims to highlight the pivotal role that computational audiology plays in moving forward hearing and balance healthcare, emphasizing the potential of AI and ML in diagnosing, treating, and preventing auditory and vestibular disorders.

The Diagnostics Revolution:
One of the most significant contributions of AI and ML in computational audiology is the revolution in diagnostics. These technologies analyze complex audiological data, such as audiograms, otoacoustic emissions, and vestibular function tests, with unprecedented precision. By detecting subtle patterns and anomalies, AI-driven diagnostics enable early identification of hearing and balance issues that might have otherwise gone unnoticed. This has particular utility in the area of hearing conservation, including occupational and ototoxic monitoring.

Real-time Adaptation with Hearing Aids:
AI-powered hearing aids can utilize machine learning to adapt to changing acoustic environments and individual preferences in real time. Whether in noisy restaurants or quiet homes, users experience optimized sound quality and speech intelligibility, thus significantly improving their quality of life. There is also significant potential for the implementation of AI tools in over-the-counter hearing aids to assess hearing and dynamically adapt to the needs of a patient.
Predictive Modeling for Balance Disorders:
Beyond hearing loss, AI and ML also play a critical role in predicting and preventing balance disorders. These technologies can analyze data from wearable sensors and vestibular function tests to identify individuals at risk of falls and vestibular disorders. Proactive interventions based on predictive modeling can reduce the incidence of falls, particularly in those at high risk.

Challenges and Ethical Considerations:
While the promise of AI and ML in computational audiology is immense, it comes with significant- and justified- concerns. Patient privacy, data security, and algorithmic bias are critical issues that demand attention. Moreover, the need for audiologists to adapt to these new technologies and ensure ethical use remains paramount. It is important to have audiologists involved in the development of these tools to ensure that patients are protected and that this emerging technology is implemented in a way that fills clinical needs.

Future Directions and Collaborations:
The future of computational audiology is bright, with ongoing advancements in AI and ML promising even more sophisticated diagnostics and treatments. Collaboration between audiologists, engineers, data scientists, and other healthcare providers will be crucial in driving this field forward. Interdisciplinary efforts can lead to the development of innovative tools and interventions that further enhance hearing and balance healthcare.

Conclusion:  
Computational audiology, empowered by AI and ML, represents a transformative force in hearing and balance healthcare. From revolutionizing diagnostics and personalized treatment plans to real-time adaptation with hearing aids and predictive modeling for balance disorders, these technologies are poised to improve patient outcomes and enhance quality of life. While challenges and ethical considerations persist, the potential for collaboration and innovation in this burgeoning field is boundless. This conference talk will shed light on the exciting prospects and critical considerations in the world of computational audiology.