ArticleJournal of robotic surgery2026
Understanding surgeon adoption of artificial intelligence surgical technology: integrating UTAUT-TOE model.
Article in Journal of robotic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Integrating Artificial Intelligence into Orthopedic Practice: Modeling Attitudes and Intentions to Use AI.Healthcare (Basel, Switzerland) · 2026Article
- Are caregivers for older adults satisfied with the care robots in the workplace? A cross-sectional study in China.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has become a transformative technology in surgical practice, revolutionizing modern healthcare and offering advancements in precision, efficiency, and patient outcomes. Despite its potential, the integration of AI technologies into surgical practice raises important professional and organizational barriers, as well as ethical concerns that must be addressed to ensure successful and morally sound integration. This research employs mixed-methods of 19 qualitative in-depth interviews with surgeons and hospital administrators, and a quantitative survey of 224 healthcare professionals across surgical specialties to investigate the acceptance of AI-enhanced surgical systems (AISS). The insights from study 1 informed the subsequent quantitative investigation in study 2, which adopts a dual-lens framework integrating the unified theory of acceptance and use of technology (UTAUT) and the technology-organization-environment (TOE). This novel approach enables examination of acceptance factors across individual and organizational levels. The findings from study 1 revealed three key themes shaping AISS acceptance, including performance, organizational, and environmental factors. Study 2 confirmed positive associations between attitude toward AISS and eight factors: performance expectancy, effort expectancy, relative advantage, compatibility, facilitating conditions, management support, social influence, and competitive pressure. Relative advantage emerged as the strongest predictor. Conversely, firm size and government pressure had significant negative effects. This research emphasizes the interdependence of individual and organizational factors influencing acceptance of AI in healthcare. It offers a roadmap for stakeholders to navigate technological integration, aligning with the objectives of the Healthy China 2030 initiative and Sustainable Development Goal 3 (SDG3): good health and well-being.
Indexed as
Identifiers
41484303What Socratic holds
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 Socratic graph.