Upcoming multi-visceral robotic surgery systems: a SAGES review
Bibliographic record
Abstract
BACKGROUND: Robotic surgical procedures continue to increase both in the United States (US) and worldwide. Several novel robotic surgical platforms are under development or undergoing regulatory approval. This review explores robotic platforms that are expected to reach US consumers within the next 2-3 years. METHODS: The SAGES Robotic Platforms Working Group identified robotic surgery platforms in various stages of development and selected multi-visceral systems nearing or completing the US Food and Drug Administration (FDA) approval process. We outline key system components including architecture, unique features, development status, regulatory approval, and expected markets. RESULTS: We identified twenty robotic platforms that met our selection criteria. Ten companies were based in North America, and ten were based in Europe or Asia. Each system is described in detail and key features are summarized in table form for easy comparison. CONCLUSION: The emergence of novel robotic surgical platforms represents an important evolution in the growth of minimally invasive surgery. Increased competition has the potential to bring value to surgical patients by stimulating innovation and driving down cost. The impact of these platforms remains to be determined, but the continued growth of robotic surgery seems to be all but assured.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".