ReviewJournal of robotic surgery2025
AI-powered robotic surgery: transforming surgical decisions.
Review in Journal of robotic surgery, 2025. 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.
- Intelligent therapeutic robotic-assisted surgery as the next frontier of precision oncology.Frontiers in oncology · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-powered robotic surgery is reshaping the surgical landscape by enhancing decision-making, precision, and patient safety in complex procedures. Despite its remarkable progress, current literature reveals critical gaps in interoperability, ethical governance, surgeon training, and real-time data integration between AI systems and human judgment. This study aims to examine how AI-powered robotic systems transform intraoperative decision-making and optimize surgical outcomes across minimally invasive, oncological, and cardiac operations. The review synthesizes quantitative insights from performance metrics and qualitative evaluations from clinical experiences, integrating multidisciplinary studies on intelligent robotics, augmented reality (AR), and machine learning in surgery. Comparative analysis highlights significant advancements in automation, preoperative planning, and intraoperative visualization, while revealing contradictions between human-centric adaptability and machine autonomy. Findings underscore that AI integration enhances surgical precision, reduces complication rates, and supports predictive analytics, yet faces limitations in ethical regulation, cost-effectiveness, and surgeon acceptance. Recommendations emphasize transparent algorithmic design, cross-specialty training, and collaborative frameworks between engineers and clinicians to ensure safe adoption. The implications extend to redefining surgical education, standardizing robotic protocols, and fostering patient-centered AI governance. Limitations include the narrow empirical scope and evolving regulatory landscape. Future research should explore adaptive AI learning models, global benchmarking, and equitable access to robotic technologies. Ultimately, AI-powered robotic surgery symbolizes a fundamental transformation toward data-driven, personalized, and ethically guided healthcare transformation.
Indexed as
Identifiers
41402661What 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.