AISIM: evaluating impacts of user interface elements of an AI assisting tool

Kannika Wiratchawa, Yupaporn Wanna, Prem Junsawang, Attapol Titapun, Anchalee Techasen, Arunnit Boonrod, Vallop Laopaiboon, Nittaya Chamadol, Sahan Bulathwela and Thanapong Intharah

PLOS ONE, 2025, vol. 20, issue 5, 1-21

Abstract: While Artificial Intelligence (AI) has demonstrated human-level capabilities in many prediction tasks, collaboration between humans and machines is crucial in mission-critical applications, especially in the healthcare sector. An important factor that enables successful human-AI collaboration is the user interface (UI). This paper evaluated the UI of BiTNet, an intelligent assisting tool for human biliary tract diagnosis via ultrasound images. We evaluated the UI of the assisting tool with 11 healthcare professionals through two main research questions: 1) did the assisting tool help improve the diagnosis performance of the healthcare professionals who use the tool? and 2) how did different UI elements of the assisting tool influence the users’ decisions? To analyze the impacts of different UI elements without multiple rounds of experiments, we propose the novel AISIM strategy. We demonstrated that our proposed strategy, AISIM, can be used to analyze the influence of different elements in the user interface in one go. Our main findings show that the assisting tool improved the diagnostic performance of healthcare professionals from different levels of experience (OR = 3.326, p-value

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0322854

DOI: 10.1371/journal.pone.0322854

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