Artificial Intelligence in Breast Reconstruction: A Scoping Review of Pre-, Intra-, and Postoperative Applications

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 breast reconstruction , machine learning , artificial intelligence , large language model , deep learning , and deep inferior epigastric perforator . Eligible original studies were categorized into pre-, intra-, and postoperative applications. 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.