Use of Large Language Models to enhance Failure Mode and Effects Analysis: A Case Study

Abstract

Failure Mode and Effects Analysis (FMEA) is widely used in radiation oncology to proactively identify and mitigate risks, but it is time-consuming and depends heavily on expert experience.This study evaluated whether large language models (LLMs) can supplement traditional expert-driven FMEA by identifying novel failure modes within the Radiation Planning Assistant (RPA) workflow. Methods and MaterialsA multidisciplinary team of board-certified medical physicists, quality assurance engineers, and software developers independently used four LLMs (ChatGPT-4, Gemini

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