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

Area of Science:

• Biomedical Engineering
• Computational Biology
• Oncology

Background:

• Gynecological cancers (breast, ovarian, cervical) pose significant health challenges.
• Traditional treatments often lack personalization and optimal efficacy.
• Advancements in regenerative medicine are explored for improved therapeutic strategies.

Purpose of the Study:

• To review the application of artificial intelligence (AI) and machine learning (ML) in stem cell and gene therapy for gynecological cancers.
• To explore how AI enhances diagnostic and therapeutic strategies in regenerative medicine.
• To highlight AI's role in personalized treatment planning and outcome improvement.

Main Methods:

• Review of AI and ML applications in stem cell and gene therapy literature.
• Analysis of AI's impact on disease prediction, target identification, and treatment optimization.
• Examination of AI's role in biomarker discovery and personalized therapy development.

Main Results:

• AI enables more accurate disease progression predictions and identification of therapeutic targets.
• ML algorithms improve the efficacy and safety of stem cell and gene therapies.
• AI facilitates tailored treatments by identifying biomarkers and genetic variations.
• AI-driven regenerative medicine optimizes stem cell functions and refines treatment protocols.
• AI enhances precision in gene silencing and anti-aging interventions for combined therapies.

Conclusions:

• AI is a transformative tool for personalized and effective treatments in women's cancers.
• AI integration offers new avenues for effective cancer management through combined strategies.
• Future research is expected to expand the scope and impact of AI-driven strategies in oncology.