Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care

Rare diseases (RDs) affect 6%-8% of the global population but remain critically underserved. People living with an RD face misdiagnosis, limited treatment options, and inequitable access to specialized care. While artificial intelligence (AI) offers transformative potential in RD care, significant challenges remain. This perspective identifies five key dimensions to equitable AI application in RD care: data availability, algorithmic fairness, patient privacy, resource prioritization, and medical ethics. To address these barriers, strategies include enhancing data diversity through internationally harmonized repositories, leveraging synthetic data, and employing fairness-aware algorithms. Privacy-preserving methods safeguard sensitive genetic data while enabling collaborative research. Transparent resource-allocation frameworks and interdisciplinary governance ensure equitable distribution of AI-driven benefits, particularly in low- and middle-income countries. Ethical considerations, including patient-centered consent and dynamic risk assessments, are foundational to sustainable AI integration. By addressing these multidisciplinary challenges, AI can advance health equity, transforming RD care from fragmented and inequitable to inclusive and innovative. This paradigm shift aligns technological progress with the ethical imperative to ensure no patient is left behind in the promise of precision medicine.