Wiratchawa Kannika, Wanna Yupaporn, Junsawang Prem, Titapun Attapol, Techasen Anchalee, Boonrod Arunnit, Laopaiboon Vallop, Chamadol Nittaya, Bulathwela Sahan, Intharah Thanapong
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.
PLoS One. 2025 May 22;20(5):e0322854. doi: 10.1371/journal.pone.0322854. eCollection 2025.
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.
虽然人工智能（AI）在许多预测任务中已展现出人类水平的能力，但在关键任务应用中，人机协作至关重要，尤其是在医疗保健领域。实现成功的人机协作的一个重要因素是用户界面（UI）。本文评估了BiTNet的用户界面，BiTNet是一种通过超声图像辅助人类进行胆道诊断的智能工具。我们通过两个主要研究问题，对11名医疗保健专业人员评估了该辅助工具的用户界面：1）该辅助工具是否有助于提高使用该工具的医疗保健专业人员的诊断性能？2）该辅助工具的不同用户界面元素如何影响用户的决策？为了在不进行多轮实验的情况下分析不同用户界面元素的影响，我们提出了新颖的AISIM策略。我们证明，我们提出的AISIM策略可用于一次性分析用户界面中不同元素的影响。我们的主要研究结果表明，该辅助工具提高了不同经验水平的医疗保健专业人员的诊断性能（OR = 3.326，p值<10-15）。此外，高人工智能预测置信度和正确的人工智能关注区域提供了高于两倍的几率，即用户会遵循人工智能的建议。最后，访谈结果与实验结果一致，即当用户被分配从超声图像中诊断胆道异常时，BiTNet增强了他们的信心。
