ArticlePlastic and reconstructive surgery. Global open2026
Artificial Intelligence in Breast Reconstruction: A Scoping Review of Pre-, Intra-, and Postoperative Applications.
Article in Plastic and reconstructive surgery. Global open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has become increasingly integrated into breast reconstruction, transforming preoperative planning, intraoperative guidance, and postoperative follow-up. AI tools have shown potential to improve patient counseling, standardize imaging analysis, and predict clinical outcomes. However, current applications need further clinical integration and validation. Methods: A scoping review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. PubMed, MEDLINE, and Embase were searched independently for studies published from 2020 onward, reflecting the surge in AI innovation. Search terms included Results: Of 496 records screened, 40 studies met inclusion criteria. Most addressed preoperative use (n = 28). Large language models such as ChatGPT consistently produced readable, accurate counseling content, improved shared decision-making, and supported informed consent generation. Imaging studies using AI-based 3-dimensional scanning or magnetic resonance imaging segmentation achieved high accuracy and reduced analysis time versus manual methods. Predictive models accurately predicted complications, donor-site morbidity, radiotherapy need, and patient dissatisfaction, enabling tailored risk mitigation. Intraoperative AI was used for real-time perfusion assessment through thermal imaging and as cognitive support tools. Postoperatively, large language models enhanced clarity of discharge instructions, whereas neural networks facilitated rapid symmetry evaluation. Conclusions: AI is reshaping breast reconstruction by improving counseling, planning, and postoperative evaluation. Although evidence remains strongest in preoperative applications, intra- and postoperative use are rapidly emerging. Future efforts should prioritize multicenter prospective validation and workflow integration to ensure safe, reproducible clinical adoption.
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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.