Integrating AI Technology for Breast Cancer Detection in The Workplace: A Systematic Review

Abstract

This systematic review explores the role of artificial intelligence (AI) technologies in detecting breast cancer within work environments, emphasizing developments in imaging techniques like mammography, ultrasound, and magnetic resonance imaging (MRI). The review showcases the effectiveness of AI, notably deep learning (DL) algorithms, in boosting diagnostic precision, minimizing false positives, and improving operational efficiency. AI frameworks, including convolutional neural networks (CNNs), show great promise in supporting radiologists by enhancing both sensitivity and specificity in identifying breast cancer. Nevertheless, challenges such as data inconsistency, the risks of model overfitting, and the necessity for varied datasets impede widespread clinical implementation. Despite these hurdles, the results highlight that incorporating AI into clinical routines can transform breast cancer screening in workplace settings, facilitating earlier detection and optimizing healthcare resources. The analysis stresses the need for additional validation and training for healthcare professionals to guarantee the smooth integration of AI technology into clinical practices.