AI In Breast, Ovarian, And Uterine Cancer Treatment: A Revolution In Genomics

Abstract

Artificial intelligence (AI) is increasingly being hailed as a revolutionary paradigm shift in the field of oncology, particularly in gene therapy for breast, ovarian, and uterine cancers. The most common cancers in women around the world also have great genetic diversity, making it difficult to employ different treatments. High-throughput sequencing methods generate vast amounts of genetic data, requiring intelligent computational methods for meaningful analysis. Machine learning algorithms, deep learning algorithms, and natural language processing are increasingly being used to analyze genetic and clinical data to make decisions about cancer. This study explores the application of artificial intelligence to transform cancer genomic therapy by integrating various omics to detect potential mutations and predict response to therapy. Artificial intelligence models can be used to improve early detection with targeted therapies, cancer subtyping, and precision cancer medicine. For example, for breast cancer, predictive factors are based on HER2 and BRCA mutations. Ovarian cancer - a prognostic model for homologous recombination deficiency. In endometrial cancer, molecular subtyping and prognostic factors are provided through artificial intelligence applications. The results clearly demonstrate that AI-assisted genomic analysis has significantly improved accuracy and efficiency compared to traditional approaches. However, despite various limitations and challenges related to bias, ethics, and interpretability, AI has great potential to update cancer genomics. This study highlights the need to combine AI and genomics to further develop personalized medicine to treat different types of cancer and improve survival rates.

Ähnliche Arbeiten