Summary of Resource-efficient Medical Report Generation Using Large Language Models, by Abdullah et al.
Resource-Efficient Medical Report Generation using Large Language Models
by Abdullah, Ameer Hamza, Seong Tae Kim
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 The proposed framework leverages vision-enabled Large Language Models (LLMs) for medical report generation, achieving better or comparative performance compared to previous solutions on this task. The approach introduces a lightweight solution that can promote greater clinical automation in the medical domain by automatically writing radiology reports for chest X-ray images. The framework explores different model sizes and enhancement approaches, such as prefix tuning, to improve the text generation abilities of the LLMs. Evaluations are conducted on a prominent large-scale radiology report dataset – MIMIC-CXR. Results demonstrate the capability of the resource-efficient framework to generate patient-specific reports with strong medical contextual understanding and high precision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors by making it easier for them to write reports about chest X-ray images. Right now, this is a time-consuming task that can be prone to mistakes. The researchers propose a new way to do this using special kinds of computers called Large Language Models (LLMs). They show that their method works well and can even improve on what has been done before. They tested it on a big dataset of radiology reports and found that it generates accurate and detailed reports. |
Keywords
» Artificial intelligence » Precision » Text generation
