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image of Application of Artificial Intelligence in Stem Cells and Gene Therapy for Gynecological Cancers

Abstract

The application of artificial intelligence (AI) in stem cell and gene therapy offers significant advancements in the treatment of gynecological cancers, including breast, ovarian, and cervical cancers. This review explores how machine learning (ML) enhances both diagnostic and therapeutic strategies in regenerative medicine. AI integration allows for more accurate disease progression predictions, identification of therapeutic targets, and optimization of personalized treatment plans. Additionally, AI improves the efficacy and safety of stem cell and gene therapy approaches by facilitating the identification of biomarkers and genetic variations, enabling tailored therapies for individual patients. The use of AI-supported analytics in combined treatment strategies presents new avenues for effective cancer management. Furthermore, AI-driven regenerative medicine optimizes stem cell functions, refines treatment protocols, and contributes to the identification of less frequent biomarkers, improving prognostic algorithms and therapy outcomes. As ML targets specific molecular changes in cancer cells, they enhance the precision of gene silencing and anti-aging interventions, offering new possibilities for combined therapies. These innovations position AI as a transformative tool in the development of personalized and effective treatments for women's cancers, with future studies likely to expand the scope and impact of AI-driven strategies.

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2025-07-15
2025-12-31

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/content/journals/cscr/10.2174/011574888X374002250707044343
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Application of Artificial Intelligence in Stem Cells and Gene Therapy for Gynecological Cancers
Bentham Science Publishers ; https://doi.org/10.2174/011574888X374002250707044343
/content/journals/cscr/10.2174/011574888X374002250707044343
/content/journals/cscr/10.2174/011574888X374002250707044343
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  • Article Type:     Review Article
Keywords: gene therapy ; gynecology ; Artificial intelligence ; cancers ; stem cell therapy ; machine learning

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