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Results for "generated summaries": 6 found

Automating Protocol-Specific Chart Checking in Radiotherapy

Authors: Jiajin Fan, Ulrich Langner, Qiongge Li, Jian Liu, Wei Nie, Edwin Quashie

Affiliation: Brown University Health, Hofstra University Medical Physics Program, Inova Hospital, Inova Schar Cancer Institute, Indiana University School of Medicine, Department of Radiation Oncology

Abstract Preview: Purpose:
Chart checking in radiotherapy ensures treatment plans meet clinical and safety standards. For patients in clinical trials, protocol-specific requirements add complexity, making manual rev...

Can AI-Based Llms be Your Study Buddy for ABR Professional Exams?

Authors: Arjit K. Baghwala, Sunan Cui, Jessica Fagerstrom, Eric C. Ford, Kristi Rae Gayle Hendrickson, Sharareh Koufigar, Samuel Ming Ho Luk, Bishwambhar Sengupta, Afua A. Yorke

Affiliation: University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiation Oncology, University of Vermont Medical Center, University of Washington and Fred Hutchinson Cancer Center, Houston Methodist Hospital

Abstract Preview: Purpose: The global burden of cancer continues to rise, leading to an increased workload in radiation oncology clinics. This surge is not only due to the growing demand for treatment machines and moda...

Chat with Oncology Information System Via Large Language Model

Authors: Michael Dohopolski, Xuejun Gu, Hao Jiang, Steve B. Jiang, Christopher Kabat, Jingying Lin, Weiguo Lu, Michael Tang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Neuralrad LLC, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine

Abstract Preview: Purpose: To streamline access to clinical data stored in Oncology Information Systems such as MOSAIQ or ARIA, we developed an AI-powered chatbot capable of querying, summarizing, and interactively ans...

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...

Multimodal Attention Fusion Model Leveraging Structured and Unstructured EHR Data for Hospital Readmission Prediction in Head and Neck Cancer

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

Abstract Preview: Purpose: Hospital readmission prediction models often rely on structured Electronic Health Record (EHR) data, overlooking critical insights from unstructured clinical notes. This study presents a mult...

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...