Advancing early breast cancer detection with artificial intelligence in low-resource healthcare systems: a narrative review
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20250656Keywords:
Artificial intelligence, Breast cancer screening, AI-enhanced mammography, Low-resource healthcare settingAbstract
Breast cancer is a leading cause of illness and death worldwide, with early detection being key to improving survival rates. However, in low-resource settings, the lack of accessible, affordable, and efficient screening methods significantly hinders timely diagnosis and intervention. Traditional breast cancer screening methods, such as mammography, are often unavailable or impractical in these regions due to high costs, inadequate infrastructure, and a shortage of trained professionals. To address these challenges, artificial intelligence (AI) technologies have emerged as promising tools to enhance breast cancer screening. AI-based solutions, such as AI-enhanced mammography, ultrasound imaging, thermography, and mobile applications, have the potential to address challenges in low-resource settings by offering cost-effective, portable, and user-friendly alternatives. These innovations can facilitate early detection, decrease diagnostic errors, and empower healthcare workers with limited training to perform screenings effectively. This review examines the role of AI in breast cancer screening, particularly in low-resource settings. It highlights the challenges associated with conventional screening methods and explores how AI can help fill these gaps. Success stories from initiatives such as RAD-AID International, Tata memorial centre, and the AI-driven ultrasound project in Rwanda demonstrate the feasibility of integrating AI tools into underserved healthcare systems. The review also discusses strategies for effective AI integration, including data collection, infrastructure development, and training. Additionally, it outlines future directions for enhancing AI applications in global health. AI has the potential to bridge the gap in breast cancer screening, ensuring that underserved populations benefit from improved early detection and better health outcomes. This review provides a comprehensive overview of AI applications in breast cancer screening and offers insights into the future of AI in low-resource healthcare systems.
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