BEYOND DETECTION: HARNESSING ARTIFICIAL INTELLIGENCE TO TRANSFORM EARLY BREAST CANCER DIAGNOSIS AND SURVIVAL OUTCOMES

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, with delayed diagnosis significantly reducing survival rates and treatment effectiveness. This study investigates the transformative role of Artificial Intelligence (AI) in enhancing early breast cancer diagnosis and improving patient survival outcomes. Grounded in the Technology Acceptance Model (TAM) and the Diffusion of Innovation Theory, the research examines how AI-driven diagnostic systems contribute to more accurate, timely, and efficient detection of breast malignancies compared to conventional screening approaches. A quantitative research design was employed using a secondary dataset comprising mammographic imaging records, clinical reports, and patient outcome data obtained from publicly available breast cancer databases. Statistical analyses, including logistic regression, predictive modeling, and machine learning performance evaluation metrics, were conducted to assess diagnostic accuracy and survival-related outcomes. The findings reveal that AI-assisted diagnostic models achieved significantly higher sensitivity, specificity, and predictive accuracy than traditional screening methods. The results indicate an average diagnostic accuracy exceeding 94%, a reduction in false-negative diagnoses by approximately 22%, and improved identification of early-stage tumors. Furthermore, survival projections demonstrated that earlier AI-supported detection was associated with increased five-year survival rates and more favorable treatment responses. The study concludes that AI has the potential to revolutionize breast cancer screening by facilitating earlier detection, reducing diagnostic errors, and enhancing patient outcomes. These measurable improvements underscore the importance of integrating AI technologies into healthcare systems to advance precision medicine and strengthen global breast cancer management strategies.