Knowledge-based learning in Text-RAG and Image-RAG
NeutralArtificial Intelligence
- A recent study analyzed the multi-modal approach in the Vision Transformer (EVA-ViT) image encoder combined with LlaMA and ChatGPT large language models (LLMs) to address hallucination issues and enhance disease detection in chest X-ray images. The research utilized the NIH Chest X-ray dataset, comparing image-based and text-based retrieval-augmented generation (RAG) methods, revealing that text-based RAG effectively mitigates hallucinations while image-based RAG improves prediction confidence.
- This development is significant as it demonstrates the potential of integrating advanced AI models to improve diagnostic accuracy in medical imaging, particularly in detecting diseases like pneumonia from chest X-rays. The findings suggest that leveraging external knowledge can enhance model reliability, which is crucial in clinical settings where accurate diagnosis is paramount.
- The study contributes to ongoing discussions about the effectiveness of AI in healthcare, particularly in addressing challenges such as data imbalance and the complexity of multi-stage structures. It highlights the importance of combining different modalities and approaches to improve AI performance, reflecting a broader trend in AI research focused on enhancing interpretability and reducing errors in critical applications.
— via World Pulse Now AI Editorial System
