Paper Type
Short
Paper Number
PACIS2026-1635
Description
As medical artificial intelligence (AI) becomes increasingly used by lay people for AI-assisted diagnosis, uncertainty disclosure is critical for promoting transparency and user trust. Drawing on the primacy effect and regulatory focus theory, this study examines how the position (beginning vs. end) and framing (prevention-focused vs. promotion-focused) of AI uncertainty disclosure shape user trust and approach-avoidance intentions toward AI-assisted diagnosis. We propose that end-positioned uncertainty disclosure enhances competence-based trust and approach-avoidance intentions but weakens integrity-based trust compared with beginning-positioned disclosure. Additionally, uncertainty framing—emphasizing potential gains (promotion-focused) or loss avoidance (prevention-focused)—moderates these effects through regulatory fit, further shaping users’ trust and approach-avoidance intentions. This research contributes to AI uncertainty communication research and offers practical guidance for designing uncertainty disclosure in healthcare AI systems.
Recommended Citation
Shan, Jialan; Deng, Zhaohua; and Deng, Zihao, "Positioning and Framing Medical AI Uncertainty: Effects on User Trust and Approach–Avoidance Intention towards AI-assisted Diagnosis" (2026). PACIS 2026 Proceedings. 13.
https://aisel.aisnet.org/pacis2026/ishealthcare/ishealthcare/13
Positioning and Framing Medical AI Uncertainty: Effects on User Trust and Approach–Avoidance Intention towards AI-assisted Diagnosis
As medical artificial intelligence (AI) becomes increasingly used by lay people for AI-assisted diagnosis, uncertainty disclosure is critical for promoting transparency and user trust. Drawing on the primacy effect and regulatory focus theory, this study examines how the position (beginning vs. end) and framing (prevention-focused vs. promotion-focused) of AI uncertainty disclosure shape user trust and approach-avoidance intentions toward AI-assisted diagnosis. We propose that end-positioned uncertainty disclosure enhances competence-based trust and approach-avoidance intentions but weakens integrity-based trust compared with beginning-positioned disclosure. Additionally, uncertainty framing—emphasizing potential gains (promotion-focused) or loss avoidance (prevention-focused)—moderates these effects through regulatory fit, further shaping users’ trust and approach-avoidance intentions. This research contributes to AI uncertainty communication research and offers practical guidance for designing uncertainty disclosure in healthcare AI systems.
Comments
14-Healthcare