Citations 2
Guest Editorial Special Issue on Advancements in Foundation Models for Medical Imaging
Liu, Tianming , Shen, Dinggang, Shen, Dinggang G. , Ye, Jong Chul , Bruijne, Marleen de , Liu, Wei
IEEE Transactions on Medical Imaging | October 27, 2025 | Volume: 44 | Issue: 10 | Pages: 3894-3897
Large Language Models on Radiation Oncology Tasks: Small Fine-Tuned Models vs. Large Vanilla Model
Wang, P. , Liu, Tianming , Wang, Peilong , Liu, Zhengliang , Holmes, Jason , Liu, Wei
No container title available | January 28, 2025 | Volume: No volume available | Issue: No issue available | Pages: No page available
10.36227/techrxiv.173808198.86649534/v1
Purpose: This study compares the performance of small, fine-tuned LLMs with the large original models on two radiation oncology clinical tasks: treatment modality selection and ICD-10 code prediction. Methods: We used data of 403 treatment modality selection cases and 377 ICD-10 code prediction cases. The models used were fine-tuned LLaMA 2 7B and Mistral 7B, as well as the larger LLaMA 3.1 70B and 405B models for comparison. We evaluated the accuracy of all models on these two tasks. Results: Both fine-tuned LLaMA 2 7B and Mistral 7B outperformed LLaMA 3.1 70B and 405B in treatment modality selection task. The fine-tuned LLaMA 2 7B and Mistral 7B models achieved accuracy comparable to the larger models in ICD-10 code prediction. Conclusions: Small models, fine-tuned with radiation oncology data, can achieve performance comparable to-or even surpass-that of larger models.