Making cancer AI fairer by fixing biased data
Algorithm-based prevention and reduction of differences in cancer outcomes arising from data imbalance
This project builds smarter AI methods so cancer tools work more fairly and accurately for people from all backgrounds.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Tennessee Health Sci Ctr NIH-funded |
| Lab location | 1 site (Memphis, United States) |
| Project ID | NIH-11168707 on NIH RePORTER |
What this research studies
Researchers will use existing cancer clinical and molecular (omics) data, including genetic information, to find when 'transfer learning' can make AI models fairer across different population groups. They will compare these transfer-learning approaches with current methods to see which reduce biased predictions that can affect cancer outcomes. The team will create and share open tools, models, and datasets so other researchers and clinicians can use and test fairer AI. These resources aim to help build clinical algorithms that give more equitable results for people of different races, ancestries, and backgrounds.
Who could benefit from this research
Good fit: Ideal candidates for contributing data are people with cancer who have clinical records and tumor molecular or genetic data, especially those from groups historically underrepresented in research.
Not a fit: Patients without molecular or genetic data available, whose care does not involve algorithm-based tools, or those seeking immediate treatment changes may not see direct benefits from this project.
Why it matters
Potential benefit: If successful, patients could benefit from more equitable AI-based diagnoses and treatment guidance that reduce disparities in cancer outcomes.
How similar studies have performed: Transfer learning and fairness methods have shown promise in other healthcare AI applications, but applying them broadly to cancer omics data and producing an open resource is relatively novel.
Where this research is happening
Memphis, United States
- University of Tennessee Health Sci Ctr — Memphis, United States (Active)
Researchers
- Principal investigator: Cui, Yan — University of Tennessee Health Sci Ctr
- Study coordinator: Cui, Yan
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.