Lyu Mengxian, Peng Cheng, Li Xiaohan, Balian Patrick, Bian Jiang, Wu Yonghui
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Cancer Informatics Shared Resource, University of Florida Health Cancer Center.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:342-349. eCollection 2025.
Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs). We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text. We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters. We compared GatorTronGPT with a previous solution based on fine-tuning of a widely used T5 model, using a clinical benchmark dataset MTS-DIALOG. The experimental results show that the GatorTronGPT-20B model achieved the best performance on all evaluation metrics. The proposed solution has a low computing cost as the LLM parameters are not updated during prompt-tuning. This study demonstrates the efficiency of generative clinical LLMs for clinical ATS through prompt tuning.
自动文本摘要（ATS）是一项新兴技术，旨在协助临床医生提供持续且协调的护理。本研究提出了一种使用生成式大语言模型（LLMs）来总结医患对话的方法。我们开发了提示调整算法，以指导生成式大语言模型总结临床文本。我们研究了提示调整策略、软提示的大小以及GatorTronGPT的少样本学习能力，GatorTronGPT是一个使用2770亿个临床和通用英语单词开发的生成式临床大语言模型，参数多达200亿个。我们使用临床基准数据集MTS-DIALOG，将GatorTronGPT与基于广泛使用的T5模型微调的先前解决方案进行了比较。实验结果表明，GatorTronGPT-20B模型在所有评估指标上均取得了最佳性能。由于在提示调整过程中不更新大语言模型参数，因此所提出的解决方案计算成本较低。本研究通过提示调整证明了生成式临床大语言模型在临床自动文本摘要方面的效率。
