Summary of D-rax: Domain-specific Radiologic Assistant Leveraging Multi-modal Data and Expert Model Predictions, by Hareem Nisar et al.
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions
by Hareem Nisar, Syed Muhammad Anwar, Zhifan Jiang, Abhijeet Parida, Ramon Sanchez-Jacob, Vishwesh Nath, Holger R. Roth, Marius George Linguraru
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large vision language models (VLMs) have made significant progress from research to practical applications. LLaVA-Med is a pioneering large language and vision assistant for biomedicine that can perform multi-modal biomedical image and data analysis, providing a natural language interface for radiologists. While it’s highly generalizable with multi-modal data, its current limitations include well-known challenges in the large language model space, such as hallucinations and imprecision in responses, which can lead to misdiagnosis and hinder clinical adaptability. To create precise and user-friendly models in healthcare, we propose D-Rax, a domain-specific, conversational radiologic assistance tool that can be used to gain insights about a particular radiologic image. We enhance the conversational analysis of chest X-ray (CXR) images to support radiological reporting, offering comprehensive insights from medical imaging and aiding in the formulation of accurate diagnosis. By fine-tuning the LLaVA-Med architecture on our curated enhanced instruction-following data, comprising images, instructions, disease diagnosis, demographic predictions, MIMIC-CXR imaging data, CXR-related visual question answer (VQA) pairs, and predictive outcomes from multiple expert AI models, we observe statistically significant improvement in responses for both open and close-ended conversations. Leveraging state-of-the-art diagnostic models combined with VLMs, D-Rax empowers clinicians to interact with medical images using natural language, potentially streamlining their decision-making process, enhancing diagnostic accuracy, and conserving time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary D-Rax is a new tool that helps doctors analyze medical images more accurately. It’s like a super smart assistant that can understand what you’re saying and show you important details about the image. This helps doctors make better diagnoses and save time. The tool uses large language models, which are really good at understanding natural language, but they also have some limitations. To fix these problems, the researchers created D-Rax by fine-tuning a big language model called LLaVA-Med on a special dataset that includes medical images and instructions. This makes D-Rax much better at understanding what doctors need to know about a particular image. The results are amazing – it works really well for both simple and complex questions, and it can even help doctors make decisions more quickly. |
Keywords
* Artificial intelligence * Fine tuning * Language model * Large language model * Multi modal
