Summary of Exploring Multilingual Large Language Models For Enhanced Tnm Classification Of Radiology Report in Lung Cancer Staging, by Hidetoshi Matsuo et al.
Exploring Multilingual Large Language Models for Enhanced TNM classification of Radiology Report in lung cancer staging
by Hidetoshi Matsuo, Mizuho Nishio, Takaaki Matsunaga, Koji Fujimoto, Takamichi Murakami
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper explores the use of large language models (LLMs) like GPT-3.5 to automate the structuring of radiology reports in natural languages. Specifically, it investigates the accuracy of TNM classification based on chest CT reports for lung cancer using GPT3.5 and evaluates the utility of multilingual LLMs in both Japanese and English. The study develops a system to automatically generate TNM classifications from radiology reports and analyzes the impact of providing full or partial TNM definitions. Results show that highest accuracy is achieved with full TNM definitions and English reports, while providing definitions for each factor statistically improves their respective accuracies. The study concludes that multilingual LLMs have potential for automatic TNM classification in radiology reports. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to help write medical reports. They want to see if these computers can do a good job of writing reports about lung cancer from X-ray pictures. They tested the computer on both English and Japanese reports, and it did better when they gave the computer more information about what to look for in each report. The results show that this computer can be helpful for doctors who need to write reports quickly. |
Keywords
» Artificial intelligence » Classification » Gpt
