转化式学习
乳腺癌
医学
医疗保健
无线电技术
叙述性评论
叙述的
人工智能
医学物理学
癌症
心理学
计算机科学
重症监护医学
内科学
放射科
政治学
教育学
语言学
哲学
法学
作者
Kauser Shaikh,Mehwish Mooghal,Abdullah Ameen,Wajiha Khan,Sana Zeeshan,Lubna Mushtaque Vohra
标识
DOI:10.47391/jpma.aku-9s-07
摘要
This narrative review explores the transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological Complete Response (pCR) in Breast Cancer (BC) patients undergoing Neo-Adjuvant Chemotherapy (NACT). Summarizing recent research findings underscores the significant strides made in the accurate assessment of pCR using AI, including deep learning and radiomics. Such AI-driven models offer promise in optimizing clinical decisions, personalizing treatment strategies, and potentially reducing the burden of unnecessary treatments, thereby improving patient outcomes. Furthermore, the review acknowledges the potential of AI to address healthcare disparities in Low- and Middle-Income Countries (LMICs), where accessible and scalable AI solutions may enhance BC management. Collaboration and international efforts are essential to fully unlock the potential of AI in BC care, offering hope for a more equitable and effective approach to treatment worldwide. Keywords: Artificial Intelligence, Learning, Breast Neoplasms, Healthcare Disparities, Neoadjuvant Therapy, Radiomics, Neoadjuvant Therapy, Pathological Response, Magnetic Resonance Imaging
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