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Please use this identifier to cite or link to this item: https://mnclhd.intersearch.com.au/mnclhdjspui/handle/123456789/398
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dc.contributor.authorSeth, I.-
dc.contributor.authorLim, B.-
dc.contributor.authorJoseph, K.-
dc.contributor.authorGracias, D.-
dc.contributor.authorXie, Y.-
dc.contributor.authorRoss, R. J.-
dc.contributor.authorRozen, W. M.-
dc.date.accessioned2025-01-08T04:40:33Z-
dc.date.available2025-01-08T04:40:33Z-
dc.date.issued2024-03-27-
dc.identifier.citationGland Surgery. 2024 Mar 27;13(3):395-411. doi: 10.21037/gs-23-414.en
dc.identifier.urihttps://mnclhd.intersearch.com.au/mnclhdjspui/handle/123456789/398-
dc.description.abstractBackground and objective: We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application. Methods: Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded. Key content and findings: AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence. Conclusions: Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.en
dc.language.isoenen
dc.subjectBreast Neoplasmsen
dc.subjectArtificial Intelligenceen
dc.subjectMammaplastyen
dc.titleUse of artificial intelligence in breast surgery: a narrative reviewen
dc.typeArticleen
dc.contributor.mnclhdauthorJoseph, Konrad-
Appears in Collections:Oncology / Cancer
Surgery

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