765 citations · 1.9k across the 87 of their papers we have counts for
12 papers · 1 filter
Applications of Large Language Models in Radiation Oncology: From Workflow Automation to Clinical Intelligence
Yuzhen Ding, Jason Holmes, Yuexing Hao +12
Large language models (LLMs) have emerged as transformative tools in medicine, with strong capabilities in language understanding, reasoning, and structured information extraction.…
Causal machine learning reveals age-dependent radiation dose effects on mandibular osteoradionecrosis
Jingyuan Chen, Yunze Yang, Olivia M. Muller +8
Distinguishing causal relationships from statistical correlations remains a fundamental challenge in clinical research, limiting the translation of observational findings into inte…
Causal Machine Learning Analysis of Empirical Relative Biological Effectiveness (RBE) for Mandible Osteoradionecrosis in Head and Neck Cancer Radiotherapy
Jingyuan Chen, Zhong Liu, Yunze Yang +8
Mandible Osteoradionecrosis (ORN) is one of the most severe adverse events (AEs) for head and neck (H&N) cancer radiotherapy. Previous retrospective investigations on real-world da…
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…
A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options
Peilong Wang, Jason Holmes, Zhengliang Liu +4
Purpose: We present an updated study evaluating the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released…
Enabling Clinical Use of Linear Energy Transfer in Proton Therapy for Head and Neck Cancer -- A Review of Implications for Treatment Planning and Adverse Events Study
Jingyuan Chen, Yunze Yang, Hongying Feng +12
Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while spari…
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…
Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report
Jason Holmes, Lian Zhang, Yuzhen Ding +7
Purpose: To introduce the concept of using large language models (LLMs) to re-label structure names in accordance with the American Association of Physicists in Medicine (AAPM) Tas…
Artificial General Intelligence for Radiation Oncology
Chenbin Liu, Zhengliang Liu, Jason Holmes +14
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can…
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…