Case study evaluates large language models enhancing FMEA in radiation oncology, suggesting improved risk identification.
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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Nair et al. (2026) studied this question.
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