Infertility Diagnosis and Treatment: A Comprehensive Review
Keywords:
Artificial Intelligence, Infertility Diagnosis, Assisted Reproductive Technology, Embryo Selection, Treatment Optimization, Personalized MedicineAbstract
Purpose: This research examines the transformative role of artificial intelligence (AI) technologies in infertility diagnosis and treatment, evaluating current applications across diagnostic procedures, assisted reproductive technologies (ART), and treatment optimization while assessing benefits, challenges, and future implications for reproductive healthcare.
Methodology: A comprehensive mixed-methods approach combining systematic literature review, primary data collection from 450 healthcare professionals across 25 fertility centers, and secondary analysis of AI implementation outcomes in reproductive medicine from 2020-2025. Statistical analysis employed descriptive statistics, correlation analysis, and regression modeling to identify relationships between AI adoption and clinical outcomes.
Major Findings: AI integration demonstrated significant improvements across multiple domains: diagnostic accuracy increased by 23-45% for various fertility assessments, ART success rates improved by 18-32% with AI-assisted embryo selection, and personalized treatment protocols showed 28% better outcomes compared to standard approaches. Key applications include automated semen analysis (92% accuracy), ovarian reserve prediction (89% accuracy), embryo viability scoring (94% accuracy), and personalized IVF protocol optimization achieving 35% higher success rates.
Conclusions: AI represents a paradigm shift in infertility diagnosis and treatment, offering unprecedented precision in diagnostic procedures, enhanced success rates in ART, and personalized treatment optimization. However, successful integration requires addressing ethical considerations, ensuring data privacy, and maintaining human clinical oversight while leveraging AI capabilities.
Significance: This study provides the first comprehensive analysis of AI applications across the entire spectrum of infertility care, offering evidence-based insights for clinicians, researchers, and policy makers developing reproductive health technologies.
