A Review of Artificial Intelligence in Predicting Outcomes, Complications, and Survival Following Breast Cancer Surgery
Year: 2026
Type: article
Study design: Systematic Review
Abstract: Background: The topic of artificial Intelligence and its application in medicine has gained tremendous popularity, coinciding with advances in its technology. Breast cancer treatments vary, with surgery being one of them. In this review, we aim to evaluate artificial Intelligence’s ability to predict surgical outcomes, complications, and survival rates after breast cancer surgery by analyzing original research conducted over the last 5 years. Materials and Methods: A PubMed search was conducted by two authors from January 1, 2020, to April 13, 2025, using the keywords "Artificial Intelligence" and "Breast Surgery". After elimination, 14 of the 215 articles were full-text screened, and nine original articles were selected for inclusion in our review. Discussion: Artificial intelligence models and systems have had a significant impact on the medical field, and breast cancer surgery is no exception. Several original research studies have explored AI's ability to predict surgical outcomes and postoperative results. In this review, we focus on the postoperative aspects of breast cancer surgery and its application of artificial intelligence. Of the nine articles included in this paper, all reported multiple statistically significant results (p <0.05). Although no prospective research directly compares artificial intelligence with practicing physicians, artificial intelligence’s ability to accurately predict surgical outcomes, complications, and survival has shown higher accuracy and c-index than commonly used prediction methods, as reported in multiple studies. Conclusion: Artificial Intelligence can be a valuable asset in helping surgeons and medical doctors worldwide predict prognosis with improved systems and accuracy rates, even surpassing those of currently used conservative systems. There is a lack of information on PubMed regarding the comparison of these two aspects, particularly with larger sample sizes. More research should be conducted on this topic, particularly in comparing Artificial Intelligence systems and models with current systems and evaluation methods.
Authors: Abdullah Emre Akşun, İsmail Canbaş, Arda Işık
Institution: Istanbul Medeniyet University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $107
OpenAlex ID: W7124912667