AI that reads eye scans and medical notes to improve diagnosis of eye disease
Natural language processing and medical imaging analysis for multi-modality computer assisted diagnosis of ophthalmic diseases
This project combines artificial intelligence with eye images and medical records to help doctors spot eye diseases earlier for people at risk of vision loss.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project ID | NIH-11181176 on NIH RePORTER |
What this research studies
From my point of view as a patient, researchers will build AI that looks at my eye scans together with information in my chart, like doctors' notes, meds, and tests. They will use natural language processing to pull important facts from free-text notes and combine that with different kinds of eye images. The team will train these multi-modality models on large datasets and test them on data from other sites to see if they work across ages, races, and genders. They will also compare methods and try fixes that make the AI more reliable and fair for different patient groups.
Who could benefit from this research
Good fit: Ideal candidates would be people with eye conditions who have digital eye imaging (for example OCT or fundus photos) and electronic health records that can be used by the research team.
Not a fit: People who do not have digital eye images or accessible medical records, or who have very rare eye conditions not represented in the data, may not benefit from this work.
Why it matters
Potential benefit: If successful, this could help detect eye problems earlier and make diagnoses more accurate and equitable, potentially reducing preventable vision loss.
How similar studies have performed: AI has already succeeded at single-image tasks like diabetic retinopathy screening, but combining multiple data types and proving consistent performance across diverse populations is newer and less established.
Where this research is happening
New Haven, United States
- Yale University — New Haven, United States (Active)
Researchers
- Principal investigator: Chen, Qingyu — Yale University
- Study coordinator: Chen, Qingyu
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.