Human Versus Machine

Michael Ayers, Daniela Sabella, Nury Ramirez, Richard A Arscott
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引用次数: 0

Abstract

This article implements a method for classifying adverse incidents involving reusable medical devices according to their underlying cause and assesses the level of agreement between different raters. To achieve this, the adverse incidents were classified into 1 or more of 62 separate categories, and the level of agreement between 3 experienced human raters was established. Moreover, the ChatGPT artificial intelligence tool was used to replicate the classification process used by human raters. The results showed that there was a fair level of agreement between human raters and a slight agreement between human raters and ChatGPT. This suggests that, although ChatGPT can intelligently classify adverse incidents, it was not able to replicate the performance of experienced human raters when given access to only the limited incident details and classification categories as provided for in this study.
人类与机器
本文实现了一种根据潜在原因对涉及可重复使用医疗器械的不良事件进行分类的方法,并评估了不同评级者之间的一致程度。为了实现这一目标,不良事件被分为62个独立类别中的1个或多个,并在3名经验丰富的人类评级员之间建立了一致的水平。此外,ChatGPT人工智能工具被用来复制人类评分者使用的分类过程。结果表明,人类评分者和ChatGPT之间有相当程度的一致,人类评分者和ChatGPT之间有轻微的一致。这表明,尽管ChatGPT可以智能地对不良事件进行分类,但在本研究中提供的有限事件细节和分类类别的访问权限下,它无法复制有经验的人类评价员的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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