A Continued Pretrained LLM Approach for Automatic Medical Note Generation
<|reference_start|>A Continued Pretrained LLM Approach for Automatic Medical Note Generation: LLMs are revolutionizing NLP tasks. However, the use of the most advanced LLMs, such as GPT-4, is often prohibitively expensive for most specialized fields. We introduce HEAL, the first continuously trained 13B LLaMA2-based LLM that is purpose-built for medical conversations and measured on automated scribing. Our results demonstrate that HEAL outperforms GPT-4 and PMC-LLaMA in PubMedQA, with an accuracy of 78.4\%. It also achieves parity with GPT-4 in generating medical notes. Remarkably, HEAL surpasses GPT-4 and Med-PaLM 2 in identifying more correct medical concepts and exceeds the performance of human scribes and other comparable models in correctness and completeness.<|reference_end|>
@article{yuan2024a,
    title={A Continued Pretrained LLM Approach for Automatic Medical Note
  Generation},
    author={Dong Yuan, Eti Rastogi, Gautam Naik, Sree Prasanna Rajagopal, Sagar
  Goyal, Fen Zhao, Bharath Chintagunta, Jeff Ward},
    journal={arXiv preprint arXiv:2403.09057},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09057},
    primaryClass={cs.CL cs.AI}
}