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A cross-sectional study on benefits, barriers, and adoption factors of artificial intelligence in the clinical laboratory

Year: 2026
Type: article
Study design: Observational Study
Abstract: Objectives: The objective of this study was to systematically assess laboratory medical professionals’ perceptions of artificial intelligence (AI). Materials and Methods: This exploratory cross-sectional study examines the opportunities, risks, and acceptance factors of AI from the perspective of laboratory medical professionals. The survey was based on standardized Likert scale questionnaires, including analytic hierarchy process (AHP) analyses. Statistical analysis: Likert scales were numerically coded, and dichotomous variables were recorded as binary values. Decisions were weighted using Saaty’s AHP, including consistency checks (consistency ratio <0.1), supplemented by descriptive statistics and non-parametric tests (Wilcoxon signed-rank test, α = 0.05). Results: The quantitative analysis showed a low level of previous experience with AI, but a predominantly positive attitude: AI was perceived significantly more often as an opportunity than as a risk (mean 4.22 vs. mean 2.5) ( p < 0.001). Participants with <20 years of professional experience consistently rated positive aspects (e.g., quality improvement and reduced workload) higher than their more experienced colleagues. An AHP opportunity analysis identified time savings as the most important benefit, followed by improved diagnostic quality and reduced workload. In the AHP risk analysis, professional uncertainty was prioritized ahead of liability issues and technological dependence. Conclusion: Laboratory medical professionals see AI primarily as an opportunity. In addition to technological requirements and foreseeable clinical benefits, legal and ethical framework conditions and employee acceptance must also be taken into account as a further dimension to drive forward AI implementations.
Language: English
Field: Medicine
Open Access status: gold
Estimated APC paid: $200
OpenAlex ID: W7225461611
FWCI:
Cites: 22
Cited by:
Related to: 10