ReviewNature reviews. Urology2026
The future of robotic surgery in the age of artificial intelligence.
Review in Nature reviews. Urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The evolution of robotic-assisted thoracic surgery: current platforms, emerging technologies, and future perspectives - a narrative review.Journal of robotic surgery · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Patient outcomes after robotic surgery vary widely, often reflecting differences in surgical performance. Artificial intelligence (AI) offers new ways to address this variability, with applications spanning automated skills assessment and feedback, intraoperative guidance and autonomous surgery. The most credible short-range advances of AI in this space consist in generating assistive systems that enhance perception, anticipate risks and standardize feedback while remaining under surgeon control. Results from early studies suggest that AI can influence decision-making, reduce errors and shorten learning curves, particularly in areas such as augmented navigation, anatomy recognition, error detection and telesurgery support. Long-term directions include emerging vision-language-action interfaces capable of programming task-specific support through natural language. In addition to technical performance, translation of AI into clinical practice will require robust datasets, systems designed around human users, regulatory alignment and clear accountability. Ultimately, the measure of surgical AI will be patient outcomes, including reduced complications, fast proficiency acquisition and improved outcome consistency across diverse settings.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.