Smarter Radiology Reporting with LLMs
Cost-effective AI assistance for radiologists using retrieval-augmented generation
This research introduces a novel approach to generate radiology reports for chest X-rays by combining large language models with multimodal retrieval techniques.
- Uses key phrase extraction to efficiently retrieve similar cases
- Reduces computational costs compared to full multimodal LLMs
- Achieves high-quality report generation without extensive training
- Maintains clinical accuracy while being resource-efficient
Why it matters: This approach could significantly reduce radiologists' workload while maintaining report quality, offering a practical solution for implementing AI in clinical radiology workflows without requiring massive computational resources.