What question did this study set out to answer?

This perspective aims to identify strategies for applying AI to improve health equity in rare disease care.

May 11, 2026Open Access

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

CLChaoyu LeiShanghai Jiao Tong University YZYing ZuoShanghai Jiao Tong University CLClaudia Abreu LopesInstituto Politécnico de Leiria

Key Points

This perspective aims to identify strategies for applying AI to improve health equity in rare disease care.
Identified five dimensions critical for equitable AI application: data availability, algorithmic fairness, patient privacy, resource prioritization, and medical ethics.
Proposed strategies including enhancing data diversity, leveraging synthetic data, and employing fairness-aware algorithms.
Emphasized the importance of privacy-preserving methods and transparent resource allocation frameworks.
Highlighted the need for globally harmonized data repositories to enhance diversity.
Outlined that ethical guidelines must prioritize patient voices to prevent widening gaps in healthcare access.
Demonstrated how targeted use of AI could strengthen overall health systems and drive inclusive progress.

Abstract

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 THE BIGGER PICTURE Rare diseases, though individually uncommon, collectively impact hundreds of millions worldwide, often leading to prolonged diagnostic journeys, limited treatment choices, and uneven access to expert care, especially in underserved communities.Artificial intelligence (AI) is emerging as a powerful tool in medicine, capable of analyzing vast amounts of data to identify patterns, predict outcomes, and personalize treatments that could revolutionize care for these overlooked illnesses.Without careful design, however, AI risks widening existing gaps, for example, when training data overlook diverse populations or when privacy concerns hinder global collaboration, potentially leaving low-and middle-income countries or ethnic minorities further behind.By prioritizing fair algorithms, secure data sharing, and ethical guidelines that prioritize patient voices, AI can help drive inclusive progress, turning rare disease management from a patchwork of inequalities into a model of equitable precision medicine.This approach could not only improve outcomes for those affected but also strengthen overall health systems, demonstrating how technology, when aligned with social justice, can foster a more compassionate and effective global medical landscape.

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