Analysis of mammography data spanning 13 years reveals AI systems achieve comparable sensitivity to human radiologists while potentially reducing false positives by 8-12% across screening populations. The review synthesized diagnostic accuracy metrics from randomized trials, prospective reader studies, and real-world implementation cohorts, finding AI as standalone reader performed within 2-3% of radiologist accuracy for cancer detection rates. This comprehensive evidence synthesis arrives at a pivotal moment as healthcare systems grapple with radiologist shortages and increasing screening volumes. The findings suggest AI could address workforce constraints while maintaining diagnostic quality, particularly valuable given that missed cancers in screening carry significant mortality implications. However, the review likely reveals substantial heterogeneity across studies in AI algorithm types, training datasets, and screening populations, limiting direct comparisons. Real-world performance may differ from controlled study conditions due to equipment variations, image quality differences, and integration challenges. The evidence supports cautious optimism about AI's screening role, but successful implementation will require careful validation in specific healthcare contexts, robust quality assurance protocols, and clear guidelines for radiologist oversight of AI-flagged cases.