By Jonathan Mo and Nicole Jiam, M.D.

Cochlear implants (CIs) have the potential to transform lives for those with severe to profound hearing loss, but their success varies from patient to patient. Some individuals regain remarkable speech perception, while others continue to struggle.

Predicting who will benefit the most has long been a challenge, as factors like brain health, prior hearing experience, and social support play a role. Traditional models have struggled to capture this complexity, but machine learning (ML) offers a new approach by analyzing large datasets to uncover patterns that might otherwise go unnoticed.

In our recent systematic review, published in Ear & Hearing in January 2025, we examined how ML has been used to predict CI outcomes, focusing on speech perception and speech production. We screened 1,442 studies and identified 16 that applied ML techniques, covering data from 5,058 patients across different age groups.

A new systemic review evaluates whether machine learning can better predict cochlear implant outcomes by analyzing a variety of factors, such as pre-implant hearing ability, auditory nerve health, and family support. Credit: Alex Mussomeli

Our goal was to assess which approaches were most effective, which factors had the greatest influence, and what challenges remain in integrating ML into clinical practice.

Most studies focused on predicting speech perception, specifically how well patients would understand speech in different environments. The most accurate models reached prediction rates as high as 98.8 percent, identifying key factors such as pre-implant hearing ability and auditory nerve health.

Speech production was another key focus, with studies predicting how well CI users would articulate speech after implantation. Important predictors included the duration of deafness, anatomical features of the cochlear nerve, and even family support.

One clear takeaway was that ML methods generally outperformed traditional statistical models. However, in some cases, simpler approaches produced comparable results, suggesting that while ML is a powerful tool, more complex models are not always advantageous. What makes ML particularly valuable is its ability to integrate multiple predictive factors and recognize patterns that conventional methods might miss.

Despite its potential, ML still faces challenges before it can be fully integrated into clinical decision-making. One major hurdle is data standardization. Studies used different types of patient data, making it difficult to compare models or apply them universally. To make ML-driven predictions clinically useful, standardized measures for audiological, demographic, and device-related factors are likely needed.

Another challenge is ensuring that models perform well beyond their original datasets. Many models showed high accuracy in training but struggled when tested on new patient populations, underscoring the need for better validation.

Beyond technical challenges, ethical considerations must also be addressed. If ML can accurately predict CI success, how should we support patients who are predicted to have poorer outcomes? How would insurance providers use these predictions to determine coverage? These questions need careful consideration before ML can be adopted into clinical decision-making.

Even with these challenges, the potential of ML to improve CI 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.

Our review highlights the opportunities ahead, and we hope it encourages further research into how ML can help maximize the benefits of CIs to improve the lives of individuals with hearing loss.

Jonathan Mo is an M.D./Ph.D. student studying neural engineering at the University of California, Davis. Nicole T. Jiam, M.D., one of the study’s coauthors, is a neurotology and skull base surgeon at the University of California, San Francisco. A 2024–2025 Emerging Research Grants scientist, Jiam is the recipient of an Elizabeth M. Keithley, Ph.D. Early Stage Investigator Award, generously supported in part by Susan and Steve Kaufman.