Continuum and Soft Robots in Minimally Invasive Surgery: A Systematic Review
Bibliographic record
Abstract
Faster recovery, reduced trauma, and improved patient outcomes drive innovations in minimally invasive surgery (MIS). Notwithstanding significant advancements, traditional MIS tools have been limited in navigating deep anatomical pathways and offering precise control at target sites. Continuum robotics has emerged as a solution, with recent developments enabling greater flexibility and maneuverability in surgical interventions. In this review, we first highlight recent developments in mechanical-continuum robots for traditional minimally invasive surgery and then summarize the current state-of-the-art in steerable catheter-based interventions. We discuss limitations to current approaches and explore the emerging potential of soft robots as a novel strategy to address the challenge of developing versatile, highly articulated flexible tools for minimally invasive surgical interventions. We hope that this review will, on the one hand, provide an introduction and resource for students and researchers alike, and on the other hand, will stimulate discussion vis-à-vis future directions in minimally invasive surgery.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".