Summary of A Continued Pretrained Llm Approach For Automatic Medical Note Generation, by Dong Yuan et al.
A Continued Pretrained LLM Approach for Automatic Medical Note Generation
by Dong Yuan, Eti Rastogi, Gautam Naik, Sree Prasanna Rajagopal, Sagar Goyal, Fen Zhao, Bharath Chintagunta, Jeff Ward
First submitted to arxiv on: 14 Mar 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 LLMs are transforming NLP tasks, but their advanced counterparts like GPT-4 are often too costly for many specialized fields. To address this limitation, we introduce HEAL, a 13B LLaMA2-based LLM designed specifically for medical conversations and evaluated on automated scribing. Our results show that HEAL outperforms GPT-4 in PubMedQA with an accuracy of 78.4%, matching its performance in generating medical notes. Impressively, HEAL surpasses GPT-4 and Med-PaLM 2 in identifying correct medical concepts and equals the performance of human scribes and comparable models in terms of correctness and completeness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are using special AI models to help with natural language processing tasks. These advanced models can be too expensive for many fields, so a new model called HEAL was created to solve this problem. HEAL is designed specifically for medical conversations and works well on automated scribing tasks. In tests, HEAL performed better than the most advanced models in some areas and matched their performance in others. This means that HEAL can be used as a useful tool for medical professionals. |
Keywords
» Artificial intelligence » Gpt » Natural language processing » Nlp » Palm
