Many single-sided deafness patients perform perfectly on standard speech-in-quiet tests due to their healthy ear, a “ceiling effect” that masks the real-world challenges they face. We propose using more sensitive metrics that assess sound quality and music perception.
Personalized Cochlear Implant Care Grounded in Music-Based Benchmarks
Our review published in Brain Sciences in May 2025 proposes a shift in how we may evaluate and provide care to CI users: by adjusting our current speech-focused performance metrics to incorporate music perception, and by integrating personalized medicine into CI.
Can Machine Learning Predict Who Will Benefit Most From Cochlear Implants?
Despite challenges, the potential of machine learning to improve cochlear implant outcomes is clear. By refining models, improving data quality, and addressing ethical concerns, we can move toward a future where CIs are more personalized and effective.
Can a Simple Cost-Saving Method Improve Hearing Healthcare?
One cost assessment method that has been increasingly used in medical literature is called “time-driven activity-based costing.” TDABC allows for a detailed step-by-step analysis of a process and its costs, which helps identify opportunities for reducing unnecessary costs and streamlining the process.