Summary of Generative Ai Is Not Ready For Clinical Use in Patient Education For Lower Back Pain Patients, Even with Retrieval-augmented Generation, by Yi-fei Zhao et al.
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation
by Yi-Fei Zhao, Allyn Bove, David Thompson, James Hill, Yi Xu, Yufan Ren, Andrea Hassman, Leming Zhou, Yanshan Wang
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
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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 proposes a novel approach using large language models (LLMs) with Retrieval-Augmented Generation (RAG) and few-shot learning to generate tailored educational materials for patients with low back pain (LBP). The study aims to improve patient education, which is crucial for improving functionality and long-term outcomes after LBP onset and treatment. The authors utilize a novel approach that combines RAG-based LLMs with physical therapists’ manual evaluation using a Likert scale to assess the generated educational materials’ redundancy, accuracy, and completeness. Additionally, the readability of the generated education materials is evaluated using the Flesch Reading Ease score. The findings demonstrate that RAG-based LLMs outperform traditional LLMs in generating accurate, complete, and readable patient education materials with less redundancy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about improving patient education for people with back pain. It uses special computers called large language models to create personalized educational materials. These models can learn from a small amount of information and generate new content based on that. The study shows that this approach can create more accurate, complete, and readable educational materials than other methods. However, the generated materials are not yet ready for use in clinics because they need to be more relevant and detailed. This research has the potential to improve patient education and make it more personalized. |
Keywords
» Artificial intelligence » Few shot » Rag » Retrieval augmented generation
