Automation or augmentation? The impact of artificial intelligence's technological characteristics on usage intention in medical staff
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) in healthcare enhances medical staff
Area Of Science
- Medical Informatics
- Human-Computer Interaction
- Organizational Psychology
Background
- Artificial intelligence (AI) is increasingly integrated into clinical practice, necessitating an understanding of healthcare professionals' adoption intentions.
- Existing research lacks a comprehensive model explaining how different types of AI (automation vs. augmentation) influence medical staff's psychological needs and technology-task fit.
- Self-determination theory and technology-task fit theory provide a framework to explore the psychological drivers of AI adoption in medicine.
Purpose Of The Study
- To develop and validate a conceptual model examining the impact of medical AI automation and augmentation on medical staff's psychological needs, technology-task fit, and usage intention.
- To investigate the mediating roles of perceived autonomy, competence, relatedness, and technology-task fit in the relationship between AI types and usage intention.
- To offer insights into the automation-augmentation paradox within AI research in a clinical context.
Main Methods
- A cross-sectional study was conducted with 400 Chinese medical staff.
- Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the data.
- The study assessed the relationships between AI automation/augmentation, psychological needs (autonomy, competence, relatedness), technology-task fit, and AI usage intention.
Main Results
- Medical AI automation positively correlated with perceived autonomy and competence.
- Medical AI augmentation positively correlated with perceived autonomy, competence, and relatedness.
- Perceived autonomy, competence, and technology-task fit sequentially mediated the effect of AI automation on usage intention. Autonomy, competence, relatedness, and technology-task fit sequentially mediated the effect of AI augmentation on usage intention.
Conclusions
- Both AI automation and augmentation positively influence medical staff's psychological needs and technology-task fit, ultimately driving usage intention.
- The findings highlight the importance of considering both automation and augmentation aspects of AI and their differential psychological impacts.
- Human-centered strategies are proposed to enhance the adoption of clinical AI by medical staff, addressing identified psychological mechanisms.
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