ReviewJournal of minimally invasive surgery2026
Artificial intelligence-driven real-time assistance in minimally invasive surgery: a technology-oriented narrative review.
Review in Journal of minimally invasive surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
Although minimally invasive surgery is a standard surgical approach because of its proven benefits to patient outcomes, it imposes increased cognitive demands on surgeons because of limited visualization, lack of tactile feedback, and constraints in instrument manipulation. These challenges have been partially mitigated by robot-assisted surgery, while recent advances in artificial intelligence (AI) have established intraoperative AI as a key technology enabling real-time support of surgical perception, decision-making, and instrument control. Here, we review the key AI technologies used during the intraoperative phase of endo-laparoscopic and robotic surgery, including anatomical and lesion recognition, instrument detection and tracking, surgical phase and workflow analysis, real-time tissue characterization, image-guided and augmented navigation, AI-assisted instrument control, and multimodal event detection. We also discuss key clinical integration challenges and future research directions focused on foundation and self-supervised learning paradigms, and human-AI collaborative system design.
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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 Socratic graph.