Summary of Fine-tuning a Local Llama-3 Large Language Model For Automated Privacy-preserving Physician Letter Generation in Radiation Oncology, by Yihao Hou et al.
Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology
by Yihao Hou, Christoph Bert, Ahmed Gomaa, Godehard Lahmer, Daniel Hoefler, Thomas Weissmann, Raphaela Voigt, Philipp Schubert, Charlotte Schmitter, Alina Depardon, Sabine Semrau, Andreas Maier, Rainer Fietkau, Yixing Huang, Florian Putz
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 study explores the use of large language models (LLMs) in generating physician letters in radiation oncology. The researchers found that base LLaMA models were inadequate for this task and instead developed an efficient method for fine-tuning LLMs using a single 48 GB GPU workstation within the hospital. This approach successfully learned radiation oncology-specific information and generated institution-specific physician letters. The study also compared the performance of different LLaMA models, finding that the 8B LLaMA-3 model outperformed the 13B LLaMA-2 model in terms of ROUGE scores. Clinical evaluations of 10 cases demonstrated the model’s ability to generate salutations, diagnoses and treatment histories, recommendations for further treatment, and planned schedules. With careful physician review and correction, automated LLM-based physician letter generation has significant practical value. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps doctors write letters faster by using special language models called large language models (LLMs). They tested different models to see which one worked best. One model was much better than the others at writing doctor’s notes that are specific to radiation oncology. The researchers showed that this model can write important parts of a letter, like the diagnosis and treatment plan. This could be very helpful for doctors who need to write many letters. |
Keywords
» Artificial intelligence » Fine tuning » Llama » Rouge
