Summary of Efficient Fine-tuning Of Large Language Models For Automated Medical Documentation, by Hui Yi Leong et al.
Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation
by Hui Yi Leong, Yi Fan Gao, Ji Shuai, Yang Zhang, Uktu Pamuksuz
First submitted to arxiv on: 14 Sep 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 paper introduces MediGen, a fine-tuned large language model designed to automate the generation of medical reports from medical dialogues. The model is built upon state-of-the-art methodologies for fine-tuning open-source pre-trained models, including LLaMA3-8B. The results show that MediGen achieves high accuracy in transcribing and summarizing clinical interactions, with a ROUGE score of 58% and a BERTScore-F1 of 72%. This suggests that MediGen has the potential to significantly reduce the administrative workload on physicians, improving both healthcare efficiency and physician well-being. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Physicians spend most of their time doing paperwork instead of helping patients. This can lead to burnout and make it harder for doctors to do their jobs effectively. To solve this problem, researchers created a computer program called MediGen that can help doctors by automatically writing medical reports. The program uses special techniques to understand what doctors are saying and write down important information. It’s very good at its job, with a score of 58% on one test and 72% on another. This means it could really help reduce the amount of time doctors spend doing paperwork and make healthcare better. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Rouge
