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Cervical cancer prediction with AI to improve efficiency of early detection and diagnosis.
Cervical cancer is a leading cause of morbidity and mortality among women, particularly in low-resource settings.
In 2022, over 78,000 women in the Americas were diagnosed with cervical cancer, leading to nearly 36,000 deaths, highlighting significant health disparities [1].
In Colombia, it ranks as the second leading cause of cancer-related deaths among women [2].
The AI-powered predictive model analyzes synthetic data and clinical-demographic variables to detect precancerous conditions early.
By leveraging factors such as age, reproductive history, and cytology results, the model identifies high-risk patients, optimizes resource allocation, and enhances diagnostic accuracy. Its integration into digital health platforms enables equitable access to advanced diagnostic tools, particularly in underserved areas.
The model incorporates variables including patient age, sexual history, tobacco use, contraceptive methods, and sexually transmitted diseases (STDs) derived from clinical studies. This information is synthesized into a database that reflects real-world clinical data, guided by the research of Song et al., Ouh et al., Egemen et al., and insights into the role of AI in cervical cancer detection as reported by the National Cancer Institute and Hou et al.
Cervical cancer is a leading cause of morbidity and mortality among women, particularly in low-resource settings.
In 2022, over 78,000 women in the Americas were diagnosed with cervical cancer, leading to nearly 36,000 deaths, highlighting significant health disparities [1].
In Colombia, it ranks as the second leading cause of cancer-related deaths among women [2].
The AI-powered predictive model analyzes synthetic data and clinical-demographic variables to detect precancerous conditions early.
By leveraging factors such as age, reproductive history, and cytology results, the model identifies high-risk patients, optimizes resource allocation, and enhances diagnostic accuracy. Its integration into digital health platforms enables equitable access to advanced diagnostic tools, particularly in underserved areas.
Without this solution, cervical cancer detection remains limited, leading to higher mortality and treatment costs.
For instance, advanced cancer treatments can cost up to 5 times more than early detection interventions.
In Colombia, limited coverage results in missed opportunities for timely diagnoses and resource optimization [2].
Implementing this AI solution could reduce cervical cancer mortality rates by 30-40% through early detection and treatment guidance. Additionally, it addresses health inequities by democratizing access to diagnostic tools, supporting global efforts to eliminate cervical cancer as a public health issue.
The model incorporates variables including patient age, sexual history, tobacco use, contraceptive methods, and sexually transmitted diseases (STDs) derived from clinical studies. This information is synthesized into a database that reflects real-world clinical data, guided by the research of Song et al., Ouh et al., Egemen et al., and insights into the role of AI in cervical cancer detection as reported by the National Cancer Institute and Hou et al.
