Evidence map›Paper›PMID 41789220›Full record

ArticleAnnals of medicine and surgery (2012)2026

AI-guided wound closure: complementing surgical judgment.

Muhammad Khizar, Muhammad Zaib, Nukhbat-Ul Nisa, Hasiba Karimi, Hasibullah Aminpoor

Abstract readLetter
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Muhammad KhizarFaculty of Medicine, Georgian American University, Tbilisi, Georgia.ORCID https://orcid.org/0009-0005-5229-5052
Muhammad ZaibFaculty of Medicine, Georgian American University, Tbilisi, Georgia.ORCID https://orcid.org/0009-0008-7675-7161
Nukhbat-Ul NisaFaculty of Medicine, Shaikh Khalifa Bin Zayed Al Nahyan Medical and Dental College, Lahore, Pakistan.ORCID https://orcid.org/0009-0006-0957-4563
Hasiba KarimiFaculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey.ORCID https://orcid.org/0000-0002-7618-3849
Hasibullah AminpoorFaculty of Medicine, Kabul University of Medical Sciences "Abu Ali Ibn Sina", Kabul, Afghanistan.ORCID https://orcid.org/0000-0003-1817-7800

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencerobotic surgerysurgical wound closure

Identifiers

PMID41789220
PMCPMC12959815

What OpenQuestion holds

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Registered trials

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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.