Predictive Analytics and Generative AI for Optimizing Cer-vical and Breast Cancer Outcomes: A Data-Centric Approach

Abstract

This research uses a data-centric approach to examine how predictive analytics and generative AI might improve cervical and breast cancer outcomes. The main goals are early identification, personalized therapy, patient monitoring, and health inequities. A thorough secondary data evaluation synthesizes information from numerous trials to assess these new clinical oncology methods. Significant results show that predictive analytics increases risk classification and therapy tailoring, while generative AI strengthens patient profiles for targeted treatments and dynamic monitoring. By detecting patterns in underprivileged communities, data-centric initiatives reduce health inequities. According to the research, data quality issues, and physician training require improvement. Policy implications include standardized data collecting, fostering health system interoperability, and subsidizing bias-reduction efforts. With these advancements, the healthcare system can increase precision medicine, cervical and breast cancer survival, and quality of life. This study shows that predictive analytics and generative AI are essential to improving cancer treatment.

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