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Results for "locally hosted": 4 found

Evaluating the Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology

Authors: Meiyun Cao, Edward L. Clouser, Xiaoning Ding, Jason Michael Holmes, Shaw Hu, Linda L. Lam, Wendy S. Lindholm, Wei Liu, Samir H. Patel, Diego Santos Toesca, Jason Sharp, Sujay A. Vora, Peilong Wang

Affiliation: Department of Radiation Oncology, Mayo Clinic, Mayo Clinic Arizona, George Washington University

Abstract Preview: Purpose: In current clinical workflow of radiation oncology departments, therapists manually summarize CT simulation orders into summaries before the CT simulation for execution. This process signific...

Implementing a Fully Automated, Web-Based Dashboard for Tracking Patient Progression through the Radiotherapy Workflow

Authors: James M. Lamb, Jack Neylon, Dylan P. O'Connell

Affiliation: Department of Radiation Oncology, University of California, Los Angeles

Abstract Preview: Purpose: Communication is imperative to safe, accurate, and timely radiotherapy. The past decade has seen a significant shift toward higher doses and shorter fractionations, which has in turn led to i...

Improving TG-263 Target Name Compliance Using Locally-Hosted Large Language Models

Authors: Rex A. Cardan, Carlos E. Cardenas, Udbhav S. Ram

Affiliation: The University of Alabama at Birmingham, University of Alabama at Birmingham

Abstract Preview: Purpose: The AAPM TG-263 report provides nomenclature guidelines for target and normal tissue structures used in radiation oncology. Adherence to these guidelines is challenging for targets, as there ...

Utilizing Large Language Models for Efficient and Accurate Clinical Data Enrichment

Authors: Ara Alexandrian, Jessica Ashford, Jean-Guy Belliveau, Allison Dalton, Nathan Dobranski, Krystal M. Kirby, Garrett M. Pitcher, David E. Solis, Hamlet Spears, Angela M. Stam, Sotirios Stathakis, Jason Stevens, Rodney J. Sullivan, Sean Xavier Sullivan, Natalie N. Viscariello

Affiliation: Louisiana State University, Mary Bird Perkins Cancer Center, The University of Alabama at Birmingham, University of Alabama at Birmingham

Abstract Preview: Purpose: To improve retrospective risk analysis in radiation oncology by leveraging Large Language Models (LLMs) to extract richly annotated data from unstructured clinical incident reports.
Method...