ArticleAnnals of medicine and surgery (2012)2026
AI-guided wound closure: complementing surgical judgment.
Article in Annals of medicine and surgery (2012), 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.
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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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Authors and funding
5 authors.
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Abstract
Artificial intelligence (AI) is redefining the precision of surgical wound closure through innovations in deep learning, computer vision, and robotic control. These technologies enable automated wound assessment, trajectory planning, and suturing assistance, with applications emerging across the United States, the United Kingdom, Japan, and India. Yet, the art of closure balancing tension, perfusion, and tissue integrity remains a fundamentally human skill. This work additionally highlights the current gap between theoretical AI capabilities and clinical validation, clarifies tissue-specific and interpretability challenges, and outlines concrete research pathways, including prospective trials, interpretable AI, and multimodal fusion systems. This letter discusses the clinical and technical aspects of AI-guided wound closure, emphasizing the importance of maintaining surgeon oversight. It argues that AI can enhance, but not supplant, human judgment, aligning with international guidelines on transparency and safety in AI deployment. Widespread clinical adoption will require rigorous validation, equitable access, and adherence to frameworks such as the TITAN Guidelines to ensure ethical and effective integration into surgical practice.
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