A Review of Robot-Assisted Needle-Insertion Approaches in Corneal Surgeries
Bibliographic record
Abstract
Ophthalmic surgery requires micrometer-level precision due to the eye’s delicate anatomy, yet manual limitations and restricted 3D visualization make absolute accuracy challenging, driving interest in robotic and Artificial Intelligence technologies to enhance safety and precision. This is a narrative review of experimental and published studies on PubMed and Open Evidence to review the current advances, challenges, and translational potential of robotic-assisted needle insertion in corneal surgery. Topics include robotic corneal surgery platforms such as the da Vinci and custom microsurgical robots, telemanipulation, intraoperative optical coherence tomography (iOCT), and reinforcement learning applications. Recent advancements in the field have demonstrated enhanced needle insertion precision, tremor elimination, and improved visualization of needle trajectory in corneal procedures, including corneal lacerations, pterygium repairs and penetrating keratoplasties (PKs). Nonetheless, significant limitations in the state of the art persist, particularly concerning the integration of robotic systems into clinical practice in in vivo settings. Our results indicate that current studies are mostly conducted in an ex vivo setting, which introduces inherent biases and reduces the generalizability of findings to clinical practice. Additionally, the majority of these studies involve small sample sizes, limiting statistical power and the ability to draw robust conclusions. Together, these limitations highlight the need for larger, well-designed in vivo studies to validate and expand upon existing findings. This review bridges experimental innovation and clinical application, highlighting strategies to overcome current barriers in robotic corneal 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".