Fine-tuning a local LLaMA-3 large language model for automated privacy-preserving physician letter generation in radiation oncology

Fine-tuning a local LLaMA-3 large language model for automated privacy-preserving physician letter generation in radiation oncology

IntroductionGenerating physician letters is a time-consuming task in daily clinical practice.MethodsThis study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncol...

Full description

Bibliographic Details
Main Authors: Yihao Hou, Christoph Bert, Ahmed Gomaa, Godehard Lahmer, Daniel Höfler, Thomas Weissmann, Raphaela Voigt, Philipp Schubert, Charlotte Schmitter, Alina Depardon, Sabine Semrau, Andreas Maier, Rainer Fietkau, Yixing Huang, Florian Putz
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Artificial Intelligence
Subjects:
radiation oncology
data privacy
parameter-efficient fine-tuning
LLaMA
fine-tuning
physician letter
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2024.1493716/full

Internet

https://www.frontiersin.org/articles/10.3389/frai.2024.1493716/full

Similar Items