A large language model combined with specialized diagnostic agents achieved superior accuracy in identifying rare genetic conditions compared to both existing AI systems and human physicians in controlled testing. The hybrid approach demonstrated particular strength in parsing complex symptom combinations that characterize orphan diseases affecting fewer than 200,000 people globally. This diagnostic breakthrough addresses a critical healthcare gap where delayed or missed diagnoses plague patients with rare conditions for years. Traditional medical training focuses on common diseases, leaving physicians ill-equipped to recognize patterns in the estimated 7,000 known rare diseases. The AI system's ability to synthesize vast medical literature with patient presentations could dramatically reduce the average 5-7 year diagnostic odyssey faced by rare disease patients. However, the technology requires validation across diverse populations and healthcare settings before clinical deployment. The approach represents a significant step toward precision medicine, where AI augments rather than replaces clinical judgment. Success depends on integration with existing workflows and physician acceptance of AI-assisted decision making.