Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
We investigate the effectiveness of fine-tuning large language models (LLMs)
on small medical datasets for text classification and named entity recognition
tasks. Using a German cardiology report dataset and the i2b2 Smoking Challenge
dataset, we demonstrate that fine-tuning small LLMs locally on limited training
data can improve performance achieving comparable results to larger models. Our
experiments show that fine-tuning improves performance on both tasks, with
notable gains observed with as few as 200-300 training examples. Overall, the
study highlights the potential of task-specific fine-tuning of LLMs for
automating clinical workflows and efficiently extracting structured data from
unstructured medical text.
