Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives

Abstract

The use of artificial intelligence is changing the landscape of breast cancer surgery through enhanced precision, accuracy, and personalized treatment strategies. This narrative review assessed artificial intelligence applications across the surgical stages of care in breast cancer surgery, including preoperative planning, intraoperative assistance, and postoperative care. A structured literature search of PubMed, Scopus, and Web of Science over the past 15 years was conducted to identify studies on artificial intelligence applications across all stages of breast cancer surgery. In the preoperative phase, artificial intelligence contributes to tumor detection, segmentation, and surgical margin assessment through advanced imaging and predictive modeling. During surgery, real-time image-guided systems and robotic platforms powered by machine learning enable greater accuracy and intraoperative decision support. Postoperatively, artificial intelligence-driven tools aid in complication prediction, recurrence monitoring, and follow-up personalization, thereby improving patient outcomes and reducing variability in care. Integration of multimodal artificial intelligence approaches from imaging, robotics, and predictive analytics across the surgical continuum can help highlight translational gaps and evaluate clinical readiness, providing insights that have not been emphasized in previous reviews. Despite these advancements, many artificial intelligence applications remain in early research stages or have limited clinical use, facing challenges in data standardization, model interpretability, ethics, and integration into practice. Clinical impact depends on infrastructure, surgeon expertise, and regulations. Nevertheless, interdisciplinary collaboration allows artificial intelligence to enhance accuracy, reduce complications, and improve patient-centered care in breast cancer surgery.

Ähnliche Arbeiten