Using AI to improve same-day breast cancer imaging results
Official title: Using artificial intelligence to support efficient same-day diagnostic imaging in breast cancer screening
Funded at University of California Los Angeles · NIH-11125051 · Data updated 2026-11-12
This study is looking at how using artificial intelligence can make breast cancer screenings faster and more accurate, helping to reduce the stress of false alarms for patients and ensuring that those who need more tests get them quickly.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project start | September 30, 2022 |
| Project end | September 29, 2027 |
| Funding year | 2024 |
| Award (this year) | $243,361 |
| NIH project number | 4R33HS029257-03 |
| Project ID | NIH-11125051 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are women undergoing routine breast cancer screening who may experience anxiety from potential false-positive results.
✕Less likely to help
Patients who have already been diagnosed with breast cancer or those not participating in screening programs may not benefit from this research.
What this research studies
This research aims to enhance the efficiency of breast cancer screening by integrating artificial intelligence (AI) into the diagnostic imaging process. By providing immediate results at the point of care, the study seeks to reduce the number of false-positive recalls and the associated anxiety experienced by patients. The AI solution will help streamline the diagnostic workflow, allowing for quicker follow-up for those who need further evaluation. Ultimately, the goal is to improve patient satisfaction and reduce unnecessary procedures.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate breast cancer screening, reducing anxiety and unnecessary procedures for patients.
How similar studies have gone: Other studies have shown promise in using AI for diagnostic imaging, indicating that this approach could be effective in improving screening outcomes.
Where this research is happening
-
University of California Los Angeles
Los Angeles, United States Active
Researchers
- Principal investigator: Hsu, William — University of California Los Angeles
- Study coordinator: Hsu, William
Related research
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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.