149 citations · 184 across the 6 of their papers we have counts for
5 papers · 1 filter
Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology
Peilong Wang, Zhengliang Liu, Yiwei Li +12
Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable cap…
Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability
Lian Zhang, Jason M. Holmes, Zhengliang Liu +11
Purpose: Prior AI-based dose prediction studies in photon and proton therapy often neglect underlying physics, limiting their generalizability to handle outlier clinical cases, esp…
RadOnc-GPT: A Large Language Model for Radiation Oncology
Zhengliang Liu, Peilong Wang, Yiwei Li +12
This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on a large dataset of radiation…
Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy
Lian Zhang, Jason M. Holmes, Zhengliang Liu +12
Purpose: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent…
Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics
Jason Holmes, Zhengliang Liu, Lian Zhang +8
We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have…