Transforming the Landscape of Clinical Information Retrieval Using Generative Artificial Intelligence: An Application in Machine Fault Analysis.

Abstract

In a radiation oncology clinic, machine downtime can be a serious burden to the entire department. This study investigates using increasingly popular generative artificial intelligence (AI) techniques to assist medical physicists in troubleshooting linear accelerator issues. Google's NotebookLM, supplemented with background information on linear accelerator issues/solutions, was used as a machine troubleshooting assistant for this purpose. Two board-certified medical physicists evaluated the large language model'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.