Abstract
Endotracheal suctioning (ES) is a complex procedure associated with a series of actions and inherent risks, particularly in the intensive care unit (ICU). Given the importance of precise execution, it is preferable to have skilled nurses perform ES tasks. To facilitate nurse training and ensure proficiency in ES procedures, automated nursing activity recognition presents a promising solution, offering benefits in terms of cost, time, and effort. In this paper, we propose a novel approach to nurse training activity recognition for ES tasks, leveraging the capabilities of Generative Artificial Intelligence (GenAI). Specifically, we demonstrate how Large Language Models (LLMs), a subset of GenAI, can enhance the efficiency of nursing activity recognition. By employing LLMs such as OpenAI's Generative Pre-trained Transformer (ChatGPT), Google's Gemini, and Microsoft's Copilot, we aim to improve the accuracy and efficiency of our methodology. Our study identifies a clear gap in the utilization of LLMs for more accurate determination of nursing activities related to ES, with reduced human interaction. Through the integration of approaches and data features suggested by LLMs, we achieve a notable increase in accuracy from baseline 0.51 to 0.58, along with an elevated F1 score from 0.31 to 0.46. These results underscore the potential of LLMs, as a subset of GenAI, to enhance traditional problem-solving efficiency by offering robust solutions and procedures.