ArticleJTCVS open2026
Transformers in surgical artificial intelligence: A domain-stratified, study-level narrative review.
Article in JTCVS open, 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.
- Development of an Artificial Intelligence Platform for Surgical Tool Detection and Phase Recognition in Laparoscopic Distal Gastrectomy.Journal of gastric cancer · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The objective was to synthesize study-reported outcomes of transformer-based artificial intelligence systems in surgical domains and describe settings where they appear advantageous relative to nontransformer models and human benchmarks. Methods: We searched major databases: PubMed, Embase, IEEE Xplore, ScienceDirect, Google Scholar, arXiv, and Cochrane Library. Eligible studies evaluated transformer architectures in surgical/perioperative contexts (medical imaging, workflow recognition, prognosis-related modeling, or education) and reported quantitative outcomes. Because of heterogeneous tasks/metrics, we performed a domain-organized narrative synthesis. Where the same study reported transformers and nontransformers on the same dataset/metrics, we computed within-study deltas (Δ = Transformers - Nontransformers) and summarized medians and interquartile ranges alongside vote counts (T>NT/tie/T<NT). No cross-study pooling or hypothesis testing was performed. Results: Paired comparisons favored transformers in medical imaging for 15 of 20 (75.0%) with median Δ +1.13 percentage points (interquartile range, 3.70) and in workflow recognition for 28 of 34 (82.4%) with median Δ +1.75 percentage points (interquartile range, 3.28). Prognosis had sparse paired data (n = 1; Δ +3.0 percentage points; illustrative). Education favored transformers overall in 5 of 6 (83.3%) paired comparisons, driven by surgery time prediction; diagnostic education tasks were mixed. Reported advantages were task dependent and dataset specific; gains were typically single-digit percentage points in like-for-like settings. Conclusions: Transformers frequently match or exceed nontransformer baselines in surgical imaging and workflow tasks, with promising, yet heterogeneously reported, signals in prognosis and education. Translation to dependable clinical/educational impact will require standardized benchmarks, external/prospective validation, transparent comparator reporting (including human baselines), and deployment studies that address real-time operating room constraints and fairness across patient and learner groups.
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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.