Identifying factors influencing acceptance of artificial intelligence among general practitioners in Danish general practice: a cross-sectional web-based survey study
Clinical Snapshot
PICO Framework
| P — Population | Danish general practitioners and residents (n=109, with 92 completing the survey) |
| I — Intervention | Not applicable - observational study |
| C — Comparator | Subgroup comparisons by age (≥60 vs <60 years) and practice location (smaller vs larger cities) |
| O — Outcomes | Technology acceptance factors including Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Perceived Trust, Anxiety, Professional Identity, Innovativeness, and Behavioral Intention toward AI adoption |
Bottom Line
This Danish cross-sectional survey of 92 general practitioners found generally positive attitudes toward AI adoption, with high scores for performance expectancy and behavioral intention, but concerns about trust, facilitating conditions, and anxiety. While the study uses validated instruments and established theoretical frameworks, the small sample size, unclear sampling methodology, and limited statistical reporting reduce confidence in the findings. The results suggest GPs are willing to adopt AI but require attention to implementation barriers. For Australian practitioners, these findings may inform digital health strategies, though differences in healthcare systems limit direct generalisability. The study highlights the importance of addressing practitioner concerns about trust and support systems when implementing AI technologies in primary care.
Key Findings
P Value: Not reported in abstract
Effect Size: Mean scores above neutral in 7 of 11 factors, with highest scores for Medical and Non-Medical Performance Expectancy
Primary Outcome: Technology acceptance factors with generally neutral to positive attitudes toward AI
Nnt Or Sensitivity: Not applicable for cross-sectional survey design
Confidence Interval: Not reported in abstract
Clinical Application
Limited by small sample size and unclear representativeness; findings may inform implementation strategies Moderate relevance to Australian general practice, though healthcare system differences may limit direct applicability. Could inform RACGP digital health initiatives and practice management system integration General practitioners considering AI adoption in primary care settings
Abstract
BACKGROUND: Despite rapid advances in artificial intelligence (AI), its adoption in Danish general practice remains limited and decentralized, relying on individual general practitioners' (GPs') decisions. This study aimed to evaluate Danish GPs' acceptance of AI and the importance of factors derived from established technology-acceptance models. METHODS: A cross-sectional web survey was conducted from mid-September 2024 to March 2025, using a culturally adapted version of a previously validated tool. The survey included 42 items: nine background items and 33 items covering 11 factors, including Medical and Non-Medical Performance Expectancy, Effort Expectancy, Social Influence (Medical and Patient), Facilitating Conditions, Perceived Trust, Anxiety, Professional Identity, Innovativeness and Behavioral Intention. Descriptive statistics, subgroup comparisons and Cronbach's alpha were provided. RESULTS: A total of 109 GPs and residents responded, with 84.4% (n = 92) completing the survey. Attitudes toward AI were generally neutral to positive, with mean scores above neutral in seven of the 11 factors. The most positive factors measured were Medical and Non-Medical Performance Expectancy. Scores related to Behavioral Intention were also high. Lower scores appeared in Perceived Trust, Facilitating Conditions and Anxiety. Older GPs (60 years and above) reported lower scores on Non-Medical Performance Expectancy and Social Influence (Medical); GPs in smaller cities showed more positive attitudes across several factors. Cronbach's alpha indicated good internal consistency on all but two scales. CONCLUSION: In summary, Danish GPs demonstrate a strong intention to adopt AI. However, issues persist as significant barriers regarding Anxiety, Perceived Trust and Facilitating Conditions. Implementation strategies considering context and clinician demographics are recommended.
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