医学
质量保证
胸痛
医疗急救
紧急医疗服务
急诊医学
医学物理学
内科学
病理
外部质量评估
作者
Graham Brant‐Zawadzki,Brent Klapthor,Chris Ryba,Drew C. Youngquist,Brooke Burton,Helen Palatinus,Scott T. Youngquist
标识
DOI:10.1080/10903127.2024.2376757
摘要
Large language models demonstrate potential in supporting quality assurance by effectively and objectively extracting data elements. However, their accuracy in interpreting non-standardized and time-sensitive details remains inferior to human evaluators. Our findings suggest that current LLMs may best offer supplemental support to the human review processes, but their current value remains limited. Enhancements in LLM training and integration are recommended for improved and more reliable performance in the quality assurance processes.
科研通智能强力驱动
Strongly Powered by AbleSci AI