HealAI: A Healthcare LLM for Effective Medical Documentation

被引:4
|
作者
Goyal, Sagar [1 ]
Rastogi, Eti [1 ]
Rajagopal, Sree Prasanna [1 ]
Yuan, Dong [1 ]
Zhao, Fen [1 ]
Chintagunta, Jai [1 ]
Naik, Gautam [1 ]
Ward, Jeff [1 ]
机构
[1] DeepScribe Inc, San Francisco, CA 94105 USA
关键词
Large language models; medical note writing; EHR; healthcare; domain-specific LLM; prompt engineering; medical domain; finetuning; retrieval; pretraining; long context LLM;
D O I
10.1145/3616855.3635739
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Since the advent of LLM's like GPT4 everyone in various industries has been trying to harness their power. Healthcare is an industry where this is a specifically challenging problem due to the high accuracy requirements. Prompt Engineering is a common technique used to design instructions for model responses, however, its challenges lie in the fact that the generic models may not be trained to accurately execute these specific tasks. We will present our journey of developing a cost-effective medical LLM, surpassing GPT4 in medical note-writing tasks. We'll touch upon our trials with medical prompt engineering, GPT4's limitations, and training an optimized LLM for specific medical tasks. We'll showcase multiple comparisons on model sizes, training data, and pipeline designs that enabled us to outperform GPT4 with smaller models, maintaining precision, reducing biases, preventing hallucinations, and enhancing note-writing style.
引用
收藏
页码:1167 / 1168
页数:2
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