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About 1 in 8 women will develop invasive breast cancer throughout their lifetime. Prevent it by identifying potential signs at an early stage.
Breast cancer remains a significant health concern as the most prominent cancer diagnosed in women, aside from non-melanoma skin cancers. In the region, they are diagnosed in advanced stages, which significantly reducesfive-year survival rates and increases treatment costs [1].
Early detection is critical, with recommended screening techniques such as mammography and MRI pivotal in identifying cancer at treatable stages. Breast cancer's potential to metastasize heightens the urgency for prompt intervention, guided by a variety of treatments tailored to the stage and specifics of each case.
Automated Diagnosis: Deep learning models analyze mammogram images with high precision, detecting anomalies in early stages [3].
Enhanced Screening Coverage: AI platforms enable remote diagnosis through telemedicine, especially in underserved regions.
Reduced Specialist Dependence: In areas with a shortage of radiologists, AI supports diagnosis, improving efficiency.
Sources include the PanAmerican Health Organization's guidelines on breast cancer risk factors andprevention, the Colombian Ministry of Health's clinical practice guidelinesummaries on breast cancer, and the Colombian National Cancer Institute's detailedscreening and treatment guidelines.
Breast cancer remains a significant health concern as the most prominent cancer diagnosed in women, aside from non-melanoma skin cancers. In the region, they are diagnosed in advanced stages, which significantly reducesfive-year survival rates and increases treatment costs [1].
Early detection is critical, with recommended screening techniques such as mammography and MRI pivotal in identifying cancer at treatable stages. Breast cancer's potential to metastasize heightens the urgency for prompt intervention, guided by a variety of treatments tailored to the stage and specifics of each case.
Automated Diagnosis: Deep learning models analyze mammogram images with high precision, detecting anomalies in early stages [3].
Enhanced Screening Coverage: AI platforms enable remote diagnosis through telemedicine, especially in underserved regions.
Reduced Specialist Dependence: In areas with a shortage of radiologists, AI supports diagnosis, improving efficiency.
While AI implementation has an initialcost, it compensates through reduced advanced treatment expenses and improvedpatient quality of life [3].
- The prognosis after a breastcancer diagnosis has improved dramatically in high-income countries, which haveseen a 40% decrease in age-standardized breast cancer mortality between 1980and 2020, following the introduction of early detection programs andstandardized treatment protocols.
- Mortality reduction: A 30%decrease in breast cancer deaths is projected with efficient early detection [3].
- Accessibility and equity: AItools can improve access to diagnosis in remote areas, reducing disparities inhealth outcomes [4].
- Health system sustainability: By reducing theburden of advanced treatments, health systems can redistribute resources toother critical areas [3].
Sources include the PanAmerican Health Organization's guidelines on breast cancer risk factors andprevention, the Colombian Ministry of Health's clinical practice guidelinesummaries on breast cancer, and the Colombian National Cancer Institute's detailedscreening and treatment guidelines.
