Abstract

Artificial intelligence (AI) is transforming healthcare through revolutionary advances in diagnostics, precision medicine, and global health equity. This study investigates AI’s groundbreaking applications across three pivotal areas: (1) genomic medicine, where AI-powered polygenic risk scoring and CRISPR gene editing are enabling personalized therapies; (2) reproductive medicine, with AI-driven embryo selection boosting IVF success rates by 15-30 percent; and (3) global health systems, where federated learning facilitates secure, cross-border medical collaborations while preserving patient privacy. Our research reveals critical challenges impeding AI’s potential: persistent algorithmic biases (particularly in genomic datasets where minority groups experience up to 40 percent lower accuracy), complex ethical quandaries in reproductive AI applications, and fragmented regulatory landscapes that hinder international implementation. To overcome these barriers, we present a comprehensive governance framework built on four foundational pillars: rigorous AI validation standards, compulsory ethical impact assessments, worldwide capability development programs, and harmonized regulatory policies. The study emphasizes the integration of explainable AI (XAI) methodologies to ensure algorithmic transparency and fairness. By bridging technical innovation with ethical considerations, we provide actionable strategies for healthcare practitioners, AI developers, and policymakers to responsibly implement AI solutions that advance medical science while upholding the highest standards of equity and patient welfare globally.