Multi-modal AI for Medical Imaging
Research collaboration on combining medical imaging with clinical text data for improved diagnostic accuracy. Achieved 12% improvement in early disease detection.
Technologies Used
Project Overview
Led a collaborative research project combining medical imaging analysis with clinical text data to improve diagnostic accuracy. The multi-modal approach leverages both visual information from medical images and contextual information from clinical notes.
Technical Approach: • Vision transformer for medical image analysis • BERT-based clinical text processing • Cross-modal attention mechanisms • Uncertainty quantification • Explainable AI components
The system was evaluated on multiple medical conditions including cancer detection, cardiovascular disease, and neurological disorders. Results showed significant improvements in early-stage disease detection across all tested conditions.
Key Challenges
- Integrating heterogeneous data modalities
- Ensuring clinical interpretability
- Handling missing or incomplete data
Impact & Results
Published in Nature Medicine. Being evaluated for clinical trials at 3 major hospitals. Potential to improve early disease detection for millions of patients.