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: Rose Al Helo, Shengwen Deng, Sven L. Gallo, David W. Jordan
Affiliation: Department of Radiology, Radiation Safety, University Hospitals Cleveland Medical Center, University Hospitals Cleveland Medical Center, Department of Radiology, Radiation Safety, University Hospitals Cleveland Medical Center; School of Medicine, Case Western Reserve University; Department of Radiology, Louis Stokes Cleveland VA Medical Center
Abstract Preview: Purpose: From an educator perspective, preparing test questions for trainees is time-consuming and requires a lot of quality verification steps (review of stems, distractors, referencing) that can pot...
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...