ArticleDigital health
How healthcare professionals perceive artificial intelligence risks: A grounded theory exploration of antecedents, dimensions, and outcomes.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Ethical concerns toward medical artificial intelligence and acceptance intentions: a structural equation modeling analysis of the risk perception-trust pathway.Frontiers in public health · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The rapid development and widespread use of artificial intelligence (AI) in healthcare are reshaping medical services. However, influenced by the "double-edged sword" effect of AI, technical limitations and human-AI interaction uncertainties may trigger multidimensional patient safety risks. This study aims to analyze healthcare professionals' risk perception of medical AI and to construct a relevant theoretical model, thereby providing scientific evidence and practical pathways to promote safe and efficient human-AI collaboration in clinical settings. Methods: This study adopted a grounded theory approach, conducting semi-structured interviews with 18 healthcare professionals (e.g., physicians, nurses, administrators) from three tertiary hospitals in China between April and May 2025. Data were analyzed using NVivo 12.0, following open, axial, and selective coding processes to identify core categories. Results: Medical AI elicits dual behavioral outcomes based on healthcare professionals' benefit-risk perceptions. These perceptions are shaped by individual, technological, information dissemination, and organizational factors. Risk perceptions are structured across six dimensions: safety and privacy, technical efficacy, ethical and social, legal and liability, capacity development, and resource consumption. Among these, technical efficacy risks are directly related to patient safety and received the greatest attention (15/18, 83.33%). Conclusions: Healthcare professionals' risk perceptions of medical AI are dynamically constructed through clinical practice, organizational contexts, and technological evolution. The findings reveal a dynamic equilibrium between technological innovation and patient safety. Targeted optimization strategies should thus be implemented from the perspectives of technology developers, healthcare institutions, and policymakers to achieve balanced development between technological empowerment and risk control.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.