Evidence map›Paper›PMID 38601286›Full record

ReviewGland surgery2024

Use of artificial intelligence in breast surgery: a narrative review.

Ishith Seth, Bryan Lim, Konrad Joseph, Dylan Gracias, Yi Xie, Richard J Ross, Warren M Rozen

Open access · diamondAbstract readReview
In one paragraph

Review in Gland surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.9field-weighted citation impact, top 5% of its field
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

11 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
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  7. Enhancing patient education in breast surgery: artificial intelligence-powered guidance for mastopexy, augmentation, reduction, and reconstruction.Translational breast cancer research : a journal focusing on translational research in breast cancer · 2025
    Article
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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

7 authors at 6 institutions in 1 country.

Ishith SethDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0001-5444-8925
Bryan LimDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
Konrad JosephDepartment of Surgery, Port Macquarie Base Hospital, New South Wales, Australia.
Dylan GraciasDepartment of Surgery, Townsville Hospital, Queensland, Australia.
Yi XieDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
Richard J RossDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
Warren M RozenDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
Peninsula Health · AUMonash Alfred Psychiatry Research centre · AUMonash University · AUPort Macquarie Base Hospital · AUThe Alfred Hospital · AUTownsville Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background 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.

Indexed as

Artificial intelligence (AI)breast imagingbreast surgery

Identifiers

PMID38601286
PMCPMC11002485
OpenAlexW4393194929

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.