Potential Efficacy of Artificial Intelligence in Mammography for Breast Cancer Screening: Current Evidence from Meta-Analysis - Amalia, Nurlinah; Nurdiana, Farah; Pradyaputri, Naura Shafa (http://dharmais.co.id/, 2025)
Background: Artificial intelligence (AI), an advancing field of data science, has been applied in mammography screening for early detection of breast cancer in an effort to enhance screening participants' outcomes. Screening is crucial to halting the spread of breast cancer. These days, mammography is typically used in screenings conducted by radiologists. Therefore, alternative diagnostic methods are needed to provide a diagnostic solution that is efficient in terms of both time and resources. This review aims to evaluate the accuracy of AI applications in radiology, specifically in mammographic image interpretation, to determine whether AI can serve as an evidence-based recommendation for breast cancer screening. Methods: We conducted a systematic review and meta-analysis following the PRISMA guidelines. Literature searches were performed across multiple databases, including PubMed, ScienceDirect, and SpringerLink. The inclusion criteria were based on the PICOs framework, focusing on individuals at risk of breast cancer undergoing mammographic screening, where AI was used to interpret the images and compared to a radiologist. Exclusion criteria included studies involving patients with diagnosed breast cancer, non-human studies, non-English, books, paid articles, and review articles. The primary outcomes of interest were the sensitivity and specificity of AI in detecting breast cancer from mammograms. Meta-analysis was conducted using STATA software, while the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was employed to evaluate study qualityResults: A total of 2,412,102 mammograms from twenty-six studies were included in this analysis. The results indicated that AI demonstrated moderate sensitivity [84% (99.92% CI: 99.91 – 99.92)] and specificity [87% (99.97% CI: 99.97 – 99.97)] with a p-value (0.001). Conclusions: These results suggest that AI has potential as a breast cancer diagnosis tool in the future. Radiologists can become more accurate with AI algorithms, which are useful for screening, cutting down on unnecessary recall rates, and reducing effort.