Summary of Application Of Notebooklm, a Large Language Model with Retrieval-augmented Generation, For Lung Cancer Staging, by Ryota Tozuka and Hisashi Johno and Akitomo Amakawa and Junichi Sato and Mizuki Muto and Shoichiro Seki and Atsushi Komaba and Hiroshi Onishi
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging
by Ryota Tozuka, Hisashi Johno, Akitomo Amakawa, Junichi Sato, Mizuki Muto, Shoichiro Seki, Atsushi Komaba, Hiroshi Onishi
First submitted to arxiv on: 8 Oct 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 This paper investigates the application of a recent advancement in large language models (LLMs) called retrieval-augmented generation (RAG) to improve the reliability of LLMs in clinical tasks. The study focuses on NotebookLM, an RAG-equipped LLM designed for staging lung cancer. By referencing reliable external knowledge (REK), the authors aim to overcome limitations such as hallucinations and insufficient referencing inherent to traditional LLMs like ChatGPT. The paper evaluates the utility and reliability of NotebookLM in clinical settings, with implications for radiology and beyond. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how a new kind of artificial intelligence (AI) called RAG-LLM can help doctors stage lung cancer more accurately. Traditional AI models like ChatGPT have limitations that make them unreliable for medical use. The researchers are trying to fix this by making the model reference reliable external knowledge, so it doesn’t make mistakes. They tested a new model called NotebookLM and found it was better at staging lung cancer than traditional models. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
