{"title":"Transforming the Landscape of Clinical Information Retrieval Using Generative AI: An Application in Machine Fault Analysis.","authors":"Tyler Alfonzetti, Junyi Xia","doi":"10.1016/j.prro.2025.02.006","DOIUrl":null,"url":null,"abstract":"<p><p>In a radiation oncology clinic, machine downtime can be a serious burden to the entire department. This study investigates using increasingly popular generative AI techniques to assist medical physicists in troubleshooting Linear Accelerator (LINAC) issues. Google's NotebookLM, supplemented with background information on LINAC issues/solutions was used as a Machine Troubleshooting Assistant for this purpose. Two board-certified Medical Physicists evaluated the LLM's responses based on hallucination, relevancy, correctness, and completeness. Results indicated that responses improved with increasing source data context and more specific prompt construction. Keeping risk-mitigation and the inherent limitations of AI in mind, this work offers a viable, low-risk method to improve efficiency in radiation oncology. This work uses a \"Machine Troubleshooting Assistance\" application to provide an adaptable example of how radiation oncology clinics can begin using generative AI to enhance clinical efficiency.</p>","PeriodicalId":54245,"journal":{"name":"Practical Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Practical Radiation Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.prro.2025.02.006","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
In a radiation oncology clinic, machine downtime can be a serious burden to the entire department. This study investigates using increasingly popular generative AI techniques to assist medical physicists in troubleshooting Linear Accelerator (LINAC) issues. Google's NotebookLM, supplemented with background information on LINAC issues/solutions was used as a Machine Troubleshooting Assistant for this purpose. Two board-certified Medical Physicists evaluated the LLM's responses based on hallucination, relevancy, correctness, and completeness. Results indicated that responses improved with increasing source data context and more specific prompt construction. Keeping risk-mitigation and the inherent limitations of AI in mind, this work offers a viable, low-risk method to improve efficiency in radiation oncology. This work uses a "Machine Troubleshooting Assistance" application to provide an adaptable example of how radiation oncology clinics can begin using generative AI to enhance clinical efficiency.
期刊介绍：
The overarching mission of Practical Radiation Oncology is to improve the quality of radiation oncology practice. PRO''s purpose is to document the state of current practice, providing background for those in training and continuing education for practitioners, through discussion and illustration of new techniques, evaluation of current practices, and publication of case reports. PRO strives to provide its readers content that emphasizes knowledge "with a purpose." The content of PRO includes:
Original articles focusing on patient safety, quality measurement, or quality improvement initiatives
Original articles focusing on imaging, contouring, target delineation, simulation, treatment planning, immobilization, organ motion, and other practical issues
ASTRO guidelines, position papers, and consensus statements
Essays that highlight enriching personal experiences in caring for cancer patients and their families.
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