Automatic Summarization of Doctor-Patient Encounter Dialogues Using
  Large Language Model through Prompt Tuning
Mengxian Lyu Cheng Peng Xiaohan Li Patrick Balian Jiang Bian Yonghui Wu
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.