Adjusting Doctor's Reliance on AI Through Labeling for Training Data and Modification of AI Output in a Muscle Tissue Detection Task

Keito Miyake, Keito Miyake, Kumi Ozaki, Akihiro Maehigashi, Seiji Yamada, Seiji Yamada

2025

Abstract

Due to the significant advancements in artificial intelligence(AI), AI technologies are increasingly providing support in various fields. However, even if AI performs at a high level, humans refuse AI for no obvious reason and prefer to solve problems on their own. For instance, experts such as medical professionals tend to be more reluctant to rely on a medical AI’s diagnosis than on a human medical professional. This tendency leads to undertrust in AI and could affect its implementation in society. Thus, this study aims to mitigate the undertrust in AI by providing two functions from the perspective of interaction design: (a) labeling AI outputs as correct or incorrect for training data and (b) modifying AI outputs. To evaluate the effectiveness of these two functions in increasing medical professionals’ reliance on AI, we conducted an experiment involving 25 radiologists and radiographers participating in a muscle-tissue-detection task. A two-way analysis of variance was conducted to analyze their AI-usage rate. The results indicate that both functions statistically increased reliance on AI. Our novel finding is that when radiologists are enabled to control AI output by labeling results as correct or incorrect, their reliance on AI increases.

Paper Citation

in Harvard Style

Miyake K., Ozaki K., Maehigashi A. and Yamada S. (2025). Adjusting Doctor's Reliance on AI Through Labeling for Training Data and Modification of AI Output in a Muscle Tissue Detection Task. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 837-844. DOI: 10.5220/0013187500003890

in Bibtex Style

@conference{icaart25,
author={Keito Miyake and Kumi Ozaki and Akihiro Maehigashi and Seiji Yamada},
title={Adjusting Doctor's Reliance on AI Through Labeling for Training Data and Modification of AI Output in a Muscle Tissue Detection Task},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={837-844},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013187500003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Adjusting Doctor's Reliance on AI Through Labeling for Training Data and Modification of AI Output in a Muscle Tissue Detection Task
SN - 978-989-758-737-5
AU - Miyake K.
AU - Ozaki K.
AU - Maehigashi A.
AU - Yamada S.
PY - 2025
SP - 837
EP - 844
DO - 10.5220/0013187500003890
PB - SciTePress