ReviewFrontiers in surgery2026
Artificial intelligence for perioperative precision in surgical oncology.
Review in Frontiers in 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.
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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.
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0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
1 author.
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Abstract
Multimodal deep learning is advancing rapidly in surgical oncology, yet fragmented clinical data continue to constrain comprehensive understanding of tumor biology. Here we review progress in artificial intelligence (AI) for precision surgical oncology, with emphasis on its contributions to surgical quality across the preoperative, intraoperative, and postoperative continuum. Preoperatively, radiogenomic models link imaging phenotypes with molecular features to support noninvasive biopsy, neoadjuvant treatment selection, and organ-preservation strategies. Intraoperatively, real-time augmented reality, optical biopsy, and deformable image registration may improve visualization of tumor margins and functional boundaries. Postoperatively, pathomic and multi-omic frameworks may support biology-informed risk stratification and adaptive adjuvant therapy. Critical barriers include limited interpretability, unstable performance under distribution shift, and insufficient prospective evidence. Current evidence supports biologically informed AI as a promising decision-support layer; however, prospective multicenter trials, cost-effectiveness analyses, and governance safeguards are required before routine clinical adoption.
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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.