SynthesisJournal of robotic surgery2026
AI-based automated bleeding monitoring in conventional and robot-assisted laparoscopic surgery: a systematic review.
Synthesis in Journal of robotic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- AI-based automated bleeding monitoring in conventional and robot-assisted laparoscopic surgery: a systematic review.Journal of robotic surgery · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Artificial intelligence has emerged as a promising approach for improving the detection and management of intraoperative bleeding during conventional and robotic-assisted laparoscopic surgery, where delayed recognition of hemorrhage can lead to increased morbidity and procedural complexity. This review synthesizes current evidence on the use of artificial intelligence for intraoperative bleeding monitoring, with a particular focus on performance, feasibility, and clinical integration. A systematic review was conducted in accordance with PRISMA 2020 guidelines. Comprehensive searches of PubMed, Scopus, Web of Science, IEEE Xplore, Embase, and grey literature identified studies published between 2016 and 2025 that applied artificial intelligence to bleeding prediction, detection, localization, tracking, and quantitative blood-loss estimation during conventional and robotic-assisted laparoscopic surgery. Data relating to study design, model architectures, evaluation metrics, latency, and integration feasibility were extracted and summarized narratively. Across the included studies, artificial intelligence models demonstrated high detection accuracy in predominantly single-centre, retrospective, or simulation-based settings, with several approaches reporting promising real-time feasibility under controlled experimental conditions. Emerging work also explored bleeding source tracking, blood-loss estimation, and early integration into surgical workflows. However, most studies relied on small, single-center datasets and retrospective validation, limiting generalizability and clinical translation. Overall, artificial intelligence-based bleeding monitoring in conventional and robotic-assisted laparoscopic surgery shows increasing technical maturity and potential, though largely unvalidated in prospective clinical settings. Future research should prioritize large, multi-institutional datasets, prospective clinical evaluation, and optimized low-latency deployment within real surgical workflows to support safe and effective intraoperative use.
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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.