Research
AI for Healthcare
Leveraging LLMs to Accelerate Cancer Detection
Overview
This research focuses on the application of Large Language Models (LLMs) in medicine, specifically targeting the acceleration of cancer detection while significantly reducing the costs associated with data labeling. By collaborating with faculty experts, we are developing novel approaches that combine the power of modern AI with clinical expertise.
Key Focus Areas
- LLM Integration: Applying state-of-the-art language models to medical imaging and diagnostic workflows.
- Cancer Detection: Developing AI-assisted systems to identify cancerous tissues with higher accuracy and speed.
- Cost Reduction: Utilizing semi-supervised and self-supervised techniques to minimize expensive expert labeling.
- Clinical Collaboration: Working directly with medical professionals to ensure practical applicability.
Research Goals
- Improved Accuracy: Enhance diagnostic precision through multi-modal AI approaches.
- Faster Diagnosis: Reduce time from scan to diagnosis significantly.
- Scalable Solutions: Create systems deployable across different healthcare settings.
- Ethical AI: Ensure transparency and explainability in medical AI decisions.
Impact & Vision
The ultimate goal is to democratize access to high-quality cancer screening by reducing both the cost and expertise required for accurate diagnosis. This research has the potential to save countless lives by enabling earlier detection and intervention, particularly in underserved communities where specialist access is limited.