Global strategies for the diffusion of robotic surgery
Bibliographic record
Abstract
BACKGROUND: The global adoption of robotic surgery has advanced rapidly in high-income countries, yet its diffusion remains limited in resource-constrained settings due to financial, infrastructural, and educational barriers. As surgical technology evolves, there is an urgent need to promote countries' equitable access to robotic platforms worldwide. AIMS: The aim of this study was to analyze global strategies employed to promote the diffusion of robotic surgery, with a particular focus on overcoming barriers in resource-limited settings, and to provide practical insights that can guide its equitable and sustainable implementation. METHODS: This study is a multinational, policy-oriented integrative review conducted under the guidance of the Research Committee of the Society for Surgery of the Alimentary Tract in the USA (SSAT). The study integrates a bibliometric analysis, a literature review, and expert insights from diverse healthcare environments. Contributions were gathered from SSAT members. RESULTS: Robotic platforms are predominantly concentrated in North America, Western Europe, and Eastern Asia, with the USA hosting nearly 60% of all installations. Research output is similarly skewed, with few countries and institutions producing most clinical trials. Key barriers to diffusion include high costs, lack of infrastructure, limited training capacity, regulatory hurdles, and resistance among surgeons. Facilitators include public-private partnerships, philanthropic support, technology transfer, simulation platforms, and curriculum integration by professional societies. CONCLUSIONS: Achieving global equity in robotic surgery requires coordinated action across research, education, clinical practice, policy, and infrastructure. Global cooperation and innovation in implementation strategies can help bridge the current disparities and promote safe, cost-effective surgical care in underserved regions, improving patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".