ArticleNPJ digital medicine2025
Synthetic data trained open-source language models are feasible alternatives to proprietary models for radiology reporting.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Small language models in medicine.Nature biomedical engineering · 2026Article
- Evaluation of large language models for TI-RADS category extraction from free-text thyroid US reports: a comparative study with human readers.Endocrine · 2026Article
- Prediction of malignancy and metastasis of thyroid cancer by combined feature sets through advanced machine learning.BMC medical informatics and decision making · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study assessed the feasibility of using synthetic data to fine-tune various open-source LLMs for free text to structured data conversation in radiology, comparing their performance with GPT models. A training set of 3000 synthetic thyroid nodule dictations was generated to train six open-source models (Starcoderbase-1B, Starcoderbase-3B, Mistral-7B, Llama-3-8B, Llama-2-13B, and Yi-34B). ACR TI-RADS template was the target model output. The model performance was tested on 50 thyroid nodule dictations from MIMIC-III patient dataset and compared against 0-shot, 1-shot, and 5-shot performance of GPT-3.5 and GPT-4. GPT-4 5-shot and Yi-34B showed the highest performance with no statistically significant difference between the models. Various open models outperformed GPT models with statistical significance. Overall, models trained with synthetic data showed performance comparable to GPT models in structured text conversion in our study. Given privacy preserving advantages, open LLMs can be utilized as a viable alternative to proprietary GPT models.
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Registered trials
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