Mammograms and AI: Improving Accuracy, Accessibility, and Acceptance

Overview

As carcinogens have become increasingly prevalent in everyday life through the growing accumulation of microplastics, harmful chemical usage, and other factors -- so too has cancer; breast cancer accounts for 30% of all female cancers, establishing itself as the largest antagonist of health in females. Unfortunately, although huge strides have been made in possible treatment avenues-- recently, the initial diagnosis, mammograms, have been incurring worldwide false - positives, not only decreasing accuracy but also the trust and likelihood of female patients returning for future mammograms, which may have detrimentally lethal effects. As false-positives coalesce with growing burnout among radiologists, the accuracy of mammogram analysis has decreased significantly. Notably, medical professionals have attempted to combat this via deep - learning artificial intelligence programs-- that complete the task of analyzing mammogram images. Additionally, disparities have always existed between rural and urban healthcare, th ese AI programs have found considerable challenges when analyzing mammograms of rural women, who are statistically more muscular. Recognizing the multitude of issues present in the current infrastructure of breast cancer treatment, especially for rural wom en… The researcher aimed to provide a solution of an improved AI algorithm catered towards rural women and their excess muscle tissue. By crafting a program cognizant of muscle tissue AND cancer tissue, as well as implementing a survey towards rural women; the researcher’s hypothesis of improved accuracy and acceptance of AI in healthcare and an algorithm’s accuracy was supported. This research has the potential to revolutionize current breast cancer treatment processes, particularly in rural communities.