Authors: Dequan Chen, Jason Michael Holmes, Tianming Liu, Wei Liu, Zhengliang Liu, Jiajian Shen, Peilong Wang
Affiliation: Department of Radiology, Mayo Clinic, Department of Radiation Oncology, Mayo Clinic, School of Computing, University of Georgia
Abstract Preview: Purpose:
We present a study to evaluate the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released models.
Methods:
A...
Authors: Steve B. Jiang, Mu-Han Lin, Dan Nguyen, Beiqian Qi, Daniel Yang, Ying Zhang
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
Online adaptive radiotherapy (oART) is a resource-intensive workflow requiring significant time and effort required from clinicians, particularly for the online evaluation of plan quality....
Authors: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang
Affiliation: Duke University Medical Center
Abstract Preview: Purpose:
This study aims to leverage large language model (LLMs) to develop a human-in-the-loop agentic framework, enhancing the efficiency of treatment planning in radiotherapy.
Methods:
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Authors: Shreyas Anil, Jason Chan, Arushi Gulati, Yannet Interian, Hui Lin, Benedict Neo, Andrea Park, Bhumika Srinivas
Affiliation: Department of Otolaryngology Head and Neck Surgery, University of California San Francisco, Department of Data Science, University of San Francisco, Department of Radiation Oncology, University of California San Francisco, University of San Francisco
Abstract Preview: Purpose: As Large Language Models (LLMs) continue to evolve, their ability to analyze Electronic Health Record (EHR) notes for clinical decision support expands. Chain of Thought (COT) reasoning, an e...
Authors: Claus Belka, Stefanie Corradini, Christopher Kurz, Guillaume Landry, Matteo Maspero, Adrian Thummerer, Erik van der Bijl
Affiliation: Department of Radiation Oncology, LMU University Hospital, LMU Munich, Radboud University Medical Center, UMC Utrecht
Abstract Preview: Purpose: To automatically harmonize non-standardized organ-at-risk (OAR) structure names from multi-lingual, multi-institutional radiotherapy datasets using state-of-the-art open-source reasoning larg...