AISIM: evaluating impacts of user interface elements of an AI assisting tool.
Authors
Affiliations (6)
Affiliations (6)
- Visual Intelligence Laboratory, Department of Statistics, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand.
- Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand.
- Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand.
- Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
- Centre for Artificial Intelligence, University College London, London, United Kingdom.
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 <10-15). In addition, high AI prediction confidence and correct AI attention area provided higher than twice the odds that the users would follow the AI suggestion. Finally, the interview results agreed with the experimental result that BiTNet boosted the users' confidence when they were assigned to diagnose abnormality in the biliary tract from the ultrasound images.