# Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records

arXiv: https://arxiv.org/abs/2503.21349 · HTML: https://mlsanity.com/paper/2503.21349/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [An Analysis on Large Language Models in Healthcare: A Case Study of BioBERT](https://mlsanity.com/paper/2310.07282/) (2023)

## Built on by (most cited first, 2 of 2)

- [When Avatars Have Personality: Effects on Engagement and Communication in Immersive Medical Training](https://mlsanity.com/paper/2509.14132/) (2025)
- [ECG-LLM -- training and evaluation of domain-specific large language models for electrocardiography](https://mlsanity.com/paper/2510.18339/) (2025)

## Abstract (arXiv)

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.