eXplainMR: Generating Real-time Textual and Visual eXplanations to Facilitate UltraSonography Learning in MR

要旨

Mixed-Reality physical task guidance systems have the benefit of providing virtual instructions while enabling learners to interact with the tangible world. However, they are mostly built around single-path tasks and often employ visual cues for motion guidance without explanations on why an action was recommended. In this paper, we introduce eXplainMR, a mixed-reality tutoring system that teaches medical trainees to perform cardiac ultrasound. eXplainMR automatically generates subgoals for obtaining an ultrasound image that contains clinically relevant information, and textual and visual explanations for each recommended move based on the visual difference between the two consecutive subgoals. We performed a between-subject experiment (N=16) in one US teaching hospital comparing eXplainMR with a baseline MR system that offers commonly used arrow and shadow guidance. We found that after using eXplainMR, medical trainees demonstrated a better understanding of anatomy and showed more systematic reasoning when deciding on the next moves, which was facilitated by the real-time explanations provided in eXplainMR.

著者
Jingying Wang
University of Michigan, Ann Arbor, Michigan, United States
Jingjing Zhang
Computer Science and Engineering, Ann Arbor, Michigan, United States
Juana Nicoll Capizzano
Medical School, Ann Arbor, Michigan, United States
Matthew Sigakis
Medical School, Ann Arbor, Michigan, United States
Xu Wang
University of Michigan, Ann Arbor, Michigan, United States
Vitaliy Popov
University of Michigan, Ann Arbor, Michigan, United States
DOI

10.1145/3706598.3714015

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714015

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Explainable AI

G303
7 件の発表
2025-04-29 01:20:00
2025-04-29 02:50:00
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