149 citations · 199 across the 16 of their papers we have counts for
18 papers
Toward Vibe Medicine: A Self-Evolving Multi-Agent Framework for Clinical Decision Support
Qianxue Zhang, Yiming Ren, Shihuan Qin +18
In recent years, the advances of large language models and autonomous agents have revolutionized the healthcare field, facilitating diagnosis and improving treatment results. Howev…
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.…
Multimodal Fusion for Sim2real Transfer in Visual Reinforcement Learning
Zichun Xu, Jingdong Zhao, Chenyu Guo +6
Depth information is robust to scene appearance variations and inherently carries 3D spatial details. Thus, a visual backbone based on the vision transformer is proposed to fuse RG…
Critical review of patient outcome study in head and neck cancer radiotherapy
Jingyuan Chen, Yunze Yang, Chenbin Liu +8
Rapid technological advances in radiation therapy have significantly improved dose delivery and tumor control for head and neck cancers. However, treatment-related toxicities cause…
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…
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…
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
Tianyang Zhong, Zhengliang Liu, Yi Pan +74
This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, includi…
Robust Optimization for Spot Scanning Proton Therapy based on Dose-Linear Energy Transfer (LET) Volume Constraints
Jingyuan Chen, Yunze Yang, Hongying Feng +8
Purpose: Historically, spot scanning proton therapy (SSPT) treatment planning utilizes dose volume constraints and linear-energy-transfer (LET) volume constraints separately to bal…
Fast Monte Carlo Dose Calculation in Proton Therapy
Jason Holmes, Hongying Feng, Lian Zhang +3
This article examines the critical role of fast Monte Carlo dose calculations in advancing proton therapy techniques, particularly in the context of increasing treatment customizat…
The Radiation Oncology NLP Database
Zhengliang Liu, Jason Holmes, Wenxiong Liao +12
We present the Radiation Oncology NLP Database (ROND), the first dedicated Natural Language Processing (NLP) dataset for radiation oncology, an important medical specialty that has…
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…
Artificial Intelligence-Facilitated Online Adaptive Proton Therapy Using Pencil Beam Scanning Proton Therapy
Hongying Feng, Jie Shan, Carlos E. Vargas +10
We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with prostate patients. AI-based auto-segmentation tool…
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…
Segment Anything Model (SAM) for Radiation Oncology
Lian Zhang, Zhengliang Liu, Lu Zhang +10
In this study, we evaluate the performance of the Segment Anything Model (SAM) in clinical radiotherapy. Our results indicate that SAM's 'segment anything' mode can achieve clinica…
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…