Optimizing National Cancer Screening Programs using AI-Enhanced Radiology: An Approach to Promoting Public Health and Reducing Diagnostic Inequities in Underserved Regions

National screening programs play a pivotal role in reducing cancer mortality rates; however, limited access to specialized radiologists and human errors in interpreting high volumes of images remain significant challenges in underserved regions. This study aims to delineate how screening processes can be optimized by integrating Artificial Intelligence (AI) algorithms into radiological systems. Research findings indicate that the implementation of Deep Learning models not only significantly enhances diagnostic speed and screening accuracy (particularly in breast and lung cancers) but also mitigates diagnostic inequities by reducing the reliance on the physical presence of specialists in remote areas. Ultimately, this approach lowers treatment costs through early detection and serves as a strategic step toward promoting public health and ensuring health equity within the healthcare system.

کلیدواژه ها:

Artificial Intelligence ، Radiology ، Cancer Screening ، Public Health ، Health Equity ، Deep Learning