Summary of Improving Radiology Report Conciseness and Structure Via Local Large Language Models, by Iryna Hartsock and Cyrillo Araujo and Les Folio and Ghulam Rasool
Improving Radiology Report Conciseness and Structure via Local Large Language Models
by Iryna Hartsock, Cyrillo Araujo, Les Folio, Ghulam Rasool
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach is proposed to enhance radiology reporting by leveraging Large Language Models (LLMs) such as Mixtral, Mistral, and Llama. The goal is to generate concise, well-structured reports that organize information according to anatomical regions, allowing physicians to quickly locate relevant findings. The study focuses on the Mixtral model due to its superior adherence to specific formatting requirements. A novel metric, the Conciseness Percentage (CP) score, is introduced to evaluate report brevity. The experiment uses a dataset of 814 radiology reports authored by seven board-certified body radiologists at a cancer center. The results show that open-source, locally deployed LLMs can significantly improve radiology report conciseness and structure while conforming to specified formatting standards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Radiology reporting is getting a boost! Researchers are using special computer models called Large Language Models (LLMs) to make reports shorter and easier to read. The goal is to help doctors find important information quickly by organizing it in a specific way. One model, Mixtral, does this job really well because it follows rules about how to format the report. The team created a new way to measure how concise these reports are called the Conciseness Percentage (CP) score. They tested different ways of using these models and found that making them condense the report first makes it even better at following rules. |
Keywords
» Artificial intelligence » Llama
