Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning

Authors

• Mengxian Lyu
• Cheng Peng
• Xiaohan Li
• Patrick Balian
• Jiang Bian
• Yonghui Wu

Language

English

Publication Date

1-1-2025

Journal

AMIA Summits on Translational Science Proceedings

PMID

40502276

PMCID

PMC12150732

PubMedCentral® Posted Date

6-10-2025

PubMedCentral® Full Text Version

Post-print

Abstract

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.

Published Open-Access

yes

Recommended Citation

Lyu, Mengxian; Peng, Cheng; Li, Xiaohan; et al., "Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning" (2025). Faculty, Staff and Student Publications. 945.
https://digitalcommons.library.tmc.edu/uthshis_docs/945