Summary of Oretrieval Augmented Generation For 10 Large Language Models and Its Generalizability in Assessing Medical Fitness, by Yu He Ke et al.
oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness
by Yu He Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Chang-Fu Kuo, Shao-Chun Wu, Vesela P. Kovacheva, Daniel Shu Wei Ting
First submitted to arxiv on: 11 Oct 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 This research paper evaluates the accuracy and effectiveness of Retrieval Augmented Generation (RAG) models in generating preoperative instructions and determining fitness for surgery. The study uses Large Language Models (LLMs) to customize RAG models with domain-specific information, making them suitable for healthcare applications. The authors developed LLM-RAG models using 35 local and 23 international preoperative guidelines, testing them against human-generated responses. The results show that the GPT4 LLM-RAG model achieved the highest accuracy (96.4%) and produced correct instructions comparable to clinicians, with no hallucinations. The study demonstrates the potential of LLM-RAG models for preoperative healthcare tasks, highlighting their efficiency, scalability, and reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computer programs can help doctors prepare patients for surgery. The researchers used special types of computer models to generate instructions and make decisions about who is fit for surgery. They tested these models against what real doctors do and found that one model was very accurate (96.4%) and made good decisions. This study shows that computers could be a helpful tool in the future, saving time and making healthcare more efficient. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
