Evidence brief: Multi-site observational evaluation of Breast AI™-supported ultrasound risk stratification in breast cancer assessment pathways

Abstract

To evaluate the diagnostic performance metrics, referral patterns, and real-world implementation characteristics associated with a Breast AI™-supported ultrasound assessment pathway across multiple heterogeneous healthcare settings.

This prospective multi-site observational study included 1,129 women undergoing routine or symptom-driven breast assessment across five healthcare facilities in South Africa between April 2023 and April 2025. Breast AI™ was used as an adjunctive ultrasound-based clinical decision-support tool alongside standard clinical assessment and breast ultrasound imaging. Referral outcomes and histopathological diagnoses were recorded where clinically indicated. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated using predefined dichotomised Breast AI™ risk categories. Site-level comparisons were assessed using chi-square analysis.

Among 1,129 clinical assessments, 417 patients underwent referral for further diagnostic evaluation, with 405 histopathologically confirmed malignancies identified. Referral-associated malignancy rates remained relatively consistent across participating healthcare sites, with no statistically significant site-level differences observed (χ² = 1.67, p = 0.795). Diagnostic performance analysis demonstrated a sensitivity of 99.3% (95% CI: 97.8–99.8), specificity of 69.8% (95% CI: 66.3–73.1), PPV of 64.7% (95% CI: 61.2–68.0), and NPV of 99.4% (95% CI: 98.3–99.8). Higher Breast AI™ risk classifications were more frequently associated with invasive ductal carcinoma and invasive lobular carcinoma, whereas approximately 30% of ductal carcinoma in situ cases were classified within the low-risk category. The median interval between Breast AI™ assessment and histopathological confirmation was 18 days (IQR: 10–32 days).

In this prospective observational multi-site cohort, Breast AI™-supported ultrasound assessment demonstrated high observed negative predictive value and reproducible risk stratification across heterogeneous healthcare environments. However, interpretation of diagnostic accuracy metrics is limited by differential histopathological verification and the absence of long-term interval cancer follow-up among low-risk patients. The findings support the feasibility of integrating AI-supported ultrasound assessment into resource-variable clinical settings, while highlighting the need for future comparative, longitudinal, and health-economic evaluation studies

Introduction

Artificial intelligence (AI) is increasingly being integrated into radiology workflows through the development of scalable diagnostic support systems capable of assisting image interpretation and clinical risk stratification [1,2]. In breast imaging, AI-supported technologies have demonstrated potential utility in improving lesion characterization, supporting early cancer detection, and assisting clinical decision-making processes [3–6]. These technologies are of particular interest in resource-constrained healthcare settings, where shortages of specialist personnel and diagnostic infrastructure may delay diagnosis and treatment initiation.

Breast cancer remains a major global public health challenge and is a leading cause of cancer-related mortality among women worldwide [7,8]. The burden is particularly pronounced in low- and middle-income countries (LMICs), where limited access to screening programs, delayed presentation, restricted imaging availability, and shortages of trained healthcare professionals contribute to poorer clinical outcomes and advanced-stage disease at diagnosis [8–10]. Early detection remains critical for improving survival outcomes and facilitating clinical downstaging; however, conventional mammography-based screening programs are often difficult to implement sustainably in LMIC settings because of infrastructure limitations, cost constraints, and workforce shortages [8,9].