A Scoping Review of Emerging and Established Surgical Robotic PlatformsWith Applications in Urologic Surgery
Bibliographic record
Abstract
Objectives Since the introduction of the first master–slave robotic platform for surgical procedures, there have been ongoing modifications and development of new platforms, but there is still a paucity of commercially available systems. Our study aims to identify all master–slave robotic surgical platforms currently commercially available or in development around the world with applications in urologic surgery. Methods A scoping literature search was performed using PRISMA methodology to identify all relevant publications in English in PubMed, PubMed Central, and Embase, with additional information being obtained from official company websites. Results Ten robotic platforms with either proven or potential application in urologic surgery were identified: the da Vinci surgical system (Intuitive), Senhance surgical system (Transentrix), Versius Surgical (CMR Ltd), Enos surgical system (Titan Medical), Revo –I (Meere Company), MiroSurge (DLR), Avatera System (Avatera Medical), Hugo Surgical Robot (Medtronic), Ottava (J&J, Ethicon, Areus), and Hinotori (Medicaroid Corporation). Conclusions This review highlights the distinct features of emerging master–slave robotic platforms with applications in urologic surgery. Research and development are now focused on finding wider applications, improving outcomes, increasing availability, and reducing cost. Additional research is required comparing newly developed master–slave robotic platforms with those already well established.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".