Stephens Rachel, Franklin Gillian, Taylor Kevin B, Tung Kaity H, Ilchenko Vladyslav, Piracha Muhammad, Tiosano Shmuel, Hall Kendria, Ally Irshad, Elkin Peter L
Department of Biomedical Informatics, University at Buffalo, Buffalo, NY.
Department of Morphology, Clinical Pathology and Forensic Medicine at Shupyk National Healthcare University of Ukraine, Kyiv, Ukraine.
Stud Health Technol Inform. 2025 May 12;326:27-32. doi: 10.3233/SHTI250230.
We evaluated the performance of Semantic Clinical Artificial Intelligence (SCAI, pronounced Sky), a large language model (LLM) knowledge resource through usability testing. This pretest-intervention-posttest mixed-methods user interface (UI) design study investigates usability to determine whether the LLM provides a more comprehensive, efficient, and enhanced user-friendly means of delivering end user information as compared to using primary sources of information from the Internet (Web). Our analysis focused on assessing the LLM's efficiency and usability in helping users arrive at accurate and reliable outcomes, to ultimately determine its value as an innovative tool for packaging and presenting information. Usability test sessions were conducted using the cognitive walkthrough approach, via Zoom. Participants were asked to respond to scenarios using only the LLM, and then only the web, and vice versa. These sessions were followed by user feedback sessions where participants rated their experiences and responded to open-ended questions related to the overall usability and satisfaction with SCAI.
我们通过可用性测试评估了语义临床人工智能(SCAI,发音为Sky)这一大型语言模型(LLM)知识资源的性能。这项前测-干预-后测混合方法的用户界面(UI)设计研究调查了可用性,以确定与使用来自互联网(网络)的主要信息来源相比,LLM是否提供了一种更全面、高效且更具用户友好性的方式来传递终端用户信息。我们的分析重点在于评估LLM在帮助用户获得准确可靠结果方面的效率和可用性,以最终确定其作为一种用于打包和呈现信息的创新工具的价值。可用性测试环节采用认知走查法,通过Zoom进行。参与者被要求仅使用LLM应对各种场景,然后仅使用网络应对,反之亦然。这些环节之后是用户反馈环节,参与者对他们的体验进行评分,并回答与SCAI的整体可用性和满意度相关的开放式问题。