Shamir Stephanie B, Sasson Arielle L, Margolies Laurie R, Mendelson David S
Department of Diagnostic, Molecular and Interventional Radiology, The Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA.
Bioengineering (Basel). 2024 May 2;11(5):451. doi: 10.3390/bioengineering11050451.
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.
人工智能（AI）已应用于医学的多个领域，以协助患者的诊断和治疗。AI在放射学领域的应用，尤其是在乳腺成像方面，已经取得了显著进展。乳腺癌是女性癌症死亡的最重要原因之一，人们越来越关注利用AI创建更有效的乳腺癌检测方法，以提高放射科医生的准确性和效率，满足患者日益增长的需求。AI可应用于成像研究，以提高图像质量、提高解读准确性，并提高时间效率和成本效率。将AI应用于乳腺X线摄影、超声和磁共振成像（MRI），可以改善癌症检测和诊断，同时减少观察者内部和观察者之间的变异性。放射科医生与AI之间的协同效应有可能改善医疗服务不足人群的患者护理，旨在为所有人提供高质量和公平的护理。此外，AI还可以改善风险分层。此外，AI应用还可以通过识别导管原位癌（DCIS）向浸润性癌的升级风险，以及更好地预测个体患者对新辅助化疗的反应，从而对治疗产生影响。AI在术前乳腺三维模型以及提高重建移植物的存活率方面也有发展潜力。
