Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review - Kulsoom Shaikh; Mehwish Mooghal; Abdullah Ameen; Wajiha Khan; Sana Zeeshan; Lubna Mushtaq Vohra (Journal of the Pakistan Medical Association, 2024)
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