[4M1-OS-14a] OS-14

[4M1-OS-14a-05] Mitigating Algorithm Aversion in Medical Professionals: Investigating the Relationship between Psychological Factors and AI Output Usage Rates

〇Keito Miyake^1,2, Kumi Ozaki³, Seiji Yamada^2,1 (1. The Graduate University for Advanced Studies, SOKENDAI, 2. National Institute of Informatics, 3. Hamamatsu University School of Medicine)

Keywords:algorithm aversion, Human-AI Interaction, Healthcare AI

AI technology in healthcare has made remarkable progress, with continuous improvements in diagnostic support accuracy and efficiency. However, medical professionals sometimes prioritize human judgment over AI despite recognizing the high performance of the systems they use, a phenomenon known as "algorithm aversion". In healthcare settings, medical errors remain a serious concern, and algorithm aversion may lead to overlooking human errors that AI support systems could prevent. Therefore, properly addressing algorithm aversion is essential for improving safety where AI assistance is available.
This study quantitatively analyzes how psychological factors influence AI output usage rates (reliance rate) in shaping medical professionals' attitudes toward AI systems, focusing on their sense of control and responsibility. The analysis employs questionnaire items to examine correlations with reliance rates through statistical analysis. The findings are expected to guide human-AI interactions in medical settings while contributing to the theoretical foundation for addressing a crucial challenge: the collaboration between humans and AI.