Embracing AI in Healthcare? Exploring Perception and Acceptance of AI-Powered Clinical Decision Support

Diagnostic decision-making is complex, and errors affect up to 15% of emergency patients. While artificial intelligence (AI) shows potential to improve diagnostic accuracy, its acceptance remains limited due to user trust issues. This study investigates psychological and contextual factors influencing AI acceptance among both patients and physicians. Using a mixed-methods approach, we identify barriers, analyze the role of trust, and propose strategies to enhance adoption. Findings highlight concerns around physician supervision, data security, and the doctor-patient relationship. We offer practical insights to guide developers, healthcare providers, and policymakers in designing ethical AI tools that align with user needs, fostering trust and responsible integration of AI in healthcare.

Authors

Publication Details

Journal
BFH: ARBOR
Published
2025-06-01
DOI
https://doi.org/10.24451/dspace/12129
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Embracing AI in Healthcare? Exploring Perception and Acceptance of AI-Powered Clinical Decision Support

Elisa Konya-Baumbach, Samira A. Abdullahi, Gert Krummrey
BFH: ARBOR
Artificial Intelligence in Healthcare and Education
article

Embracing AI in Healthcare? Exploring Perception and Acceptance of AI-Powered Clinical Decision Support

Elisa Konya-Baumbach, Samira A. Abdullahi, Gert Krummrey
article en

Abstract

Diagnostic decision-making is complex, and errors affect up to 15% of emergency patients. While artificial intelligence (AI) shows potential to improve diagnostic accuracy, its acceptance remains limited due to user trust issues. This study investigates psychological and contextual factors influencing AI acceptance among both patients and physicians. Using a mixed-methods approach, we identify barriers, analyze the role of trust, and propose strategies to enhance adoption. Findings highlight concerns around physician supervision, data security, and the doctor-patient relationship. We offer practical insights to guide developers, healthcare providers, and policymakers in designing ethical AI tools that align with user needs, fostering trust and responsible integration of AI in healthcare.

BFH: ARBOR
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Openalex Percentile: Top 32%
Artificial Intelligence in Healthcare and Education
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Embracing AI in Healthcare? Exploring Perception and Acceptance of AI-Powered Clinical Decision Support — Elisa Konya-Baumbach, Samira A. Abdullahi, et al. · BFH: ARBOR (2025) | TGRS Research Map | TGRS