AI-Assisted Point of Care Ultrasound (POCUS) Vs. Mammography for Early Breast Cancer Detection: A Comparative Review

Background: Mammography (MG) is the current standard for population screening and mortality reduction, but sensitivity declines in dense breasts, and access can be limited in low-resource settings. Objective: Identify and synthesize published comparisons of AI-assisted ultrasound evaluation, particularly handheld/Point of Care Ultrasound (POCUS) against standard mammography strategies for early detection; summarize outcomes (sensitivity, specificity, cancer detection rate [CDR], interval cancers, recall/biopsy rates), and outline where artificial intelligence (AI) + Point of care ultrasound (POCUS) may be superior. Findings: Randomized and cohort data show Mammography + ultrasound detects more cancers and halves interval cancers versus Mammography alone (trade-off: lower specificity). Emerging AI-assisted POCUS demonstrates very high sensitivity for palpable masses on portable devices and can safely triage 38–67% of benign cases away from referral imaging. This is based on published articles and a meta-analysis review. In dense breasts, mammography-supplemental US outperforms MG+AI on several diagnostic endpoints. Nationwide real-world programs show MG+AI increases CDR over MG alone, according to a 2025 published article reported by Eisemann et al. (2025) in Nature Medicine Conclusion: Direct RCTs of AI-POCUS vs Mammography for screening are not yet published; however, across published comparative articles, ultrasound-based strategies and especially AI-assisted POCUS triage are clinically advantageous in certain medical cases, for example, dense breasts, palpable masses, low-resource settings, and are likely to be non-inferior, and sometimes superior to MG-only strategies for early detection, albeit with a specificity trade-off that AI may reduce (Ohuchi et al., 2016).