Improving fairness in AI algorithms for glaucoma treatment
Improving Fairness and Reducing Bias in Artificial Intelligence Algorithms for Glaucoma
This study is working on smart computer programs that can help doctors predict which glaucoma patients might be at risk of losing their vision, especially focusing on making sure everyone gets fair treatment, no matter their background.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-11030044 on NIH RePORTER |
What this research studies
This research focuses on developing personalized prediction algorithms to better understand and manage glaucoma, a leading cause of irreversible blindness. By utilizing artificial intelligence techniques on electronic health records, the study aims to create high-performance predictive models that can identify patients at high risk for vision loss. A key aspect of this research is addressing algorithmic bias, particularly how it affects marginalized groups, to ensure equitable healthcare outcomes. The project will leverage data from the multicenter Sight Outcomes Research Collaborative to evaluate the fairness and effectiveness of these AI algorithms.
Who could benefit from this research
Good fit: Ideal candidates for this research include glaucoma patients, particularly those from Black and Hispanic communities who are at higher risk for disease progression.
Not a fit: Patients with early-stage glaucoma or those who do not belong to marginalized communities may not receive direct benefits from this research.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions and personalized treatment plans for glaucoma patients, ultimately reducing the risk of vision loss.
How similar studies have performed: Previous research has shown success in using AI for predictive modeling in healthcare, but addressing algorithmic bias in this context is a relatively novel approach.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Wang, Sophia Ying — Stanford University
- Study coordinator: Wang, Sophia Ying
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.