Improving fairness in healthcare AI by enhancing diversity in data
Official title: Fairness in Practice: Defining and Implementing Diversity & Representation in AI datasets for Healthcare
Funded at Stanford University · NIH-10986273 · Data updated 2026-11-12
This study is looking at how to make sure that artificial intelligence in healthcare works fairly for everyone by focusing on including a wide range of people in the data it uses, so that patients can get better and more equal care.
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 start | September 1, 2024 |
| Project end | June 30, 2027 |
| Funding year | 2024 |
| Award (this year) | $499,530 |
| NIH project number | 1R01HG014227-01A1 |
| Project ID | NIH-10986273 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals from diverse backgrounds who may be affected by disparities in healthcare outcomes.
✕Less likely to help
Patients who are not part of historically underrepresented groups may not directly benefit from the focus on diversity in AI datasets.
What this research studies
This research investigates how to ensure fairness in artificial intelligence systems used in healthcare by focusing on the diversity and representation of data. It aims to understand the concepts of diversity and representation among researchers and how these concepts influence the implementation of AI systems. By examining existing initiatives like the All of Us Research Program, the study seeks to identify gaps and propose methods to improve the inclusivity of datasets used in healthcare AI. Patients may benefit from more equitable healthcare solutions that arise from AI systems trained on diverse and representative data.
Why it matters
Potential benefit: If successful, this research could lead to more equitable healthcare AI systems that provide better outcomes for all patient groups.
How similar studies have gone: Previous research has shown that improving diversity in datasets can enhance the performance and fairness of AI systems, indicating a promising direction for this work.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Martinez-Martin, Nicole a — Stanford University
- Study coordinator: Martinez-Martin, Nicole a
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.