Transformers in surgical artificial intelligence: A domain-stratified, study-level narrative review

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