Summary of Best Practices For Large Language Models in Radiology, by Christian Bluethgen et al.
Best Practices for Large Language Models in Radiology
by Christian Bluethgen, Dave Van Veen, Cyril Zakka, Katherine Link, Aaron Fanous, Roxana Daneshjou, Thomas Frauenfelder, Curtis Langlotz, Sergios Gatidis, Akshay Chaudhari
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 abstract presents a review on the potential applications of large language models (LLMs) in radiology, focusing on integrating complex imaging data with clinical information to produce actionable insights. It highlights the importance of nuanced language application for managing requests, interpreting findings, and documenting outcomes. The paper explores best practices for optimizing LLM characteristics for radiology practices, including effective prompting and fine-tuning strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have the potential to improve radiology by integrating complex imaging data with clinical information. This means that doctors can better understand patient images and make more accurate diagnoses. The review discusses how to use LLMs in radiology, including how to optimize their characteristics for this field. |
Keywords
» Artificial intelligence » Fine tuning » Prompting
