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 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.

Authors

Institutions

Publication Details

Journal
Patterns
Published
2026-05-01
DOI
https://doi.org/10.1016/j.patter.2026.101535
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

T Y Alvin Liu, Heaven Yeshaneh Tatere, Chaoyu Lei, Lufa Zhang et al.
Patterns
Genomics and Rare Diseases
article

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

T Y Alvin Liu, Heaven Yeshaneh Tatere, Chaoyu Lei, Lufa Zhang, Zilong Wang, Kang Dang, Ying Zuo, Claudia Abreu Lopes, Kai Jin, Benjamin Xu, Haoxuan Yu, Thomas Ploug, Ning Zhang, Huifang Zhou, Jaimee Stuart, Fanyi Kong
article en

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.

PatternsVol. 7(5)
University of Southern California (US), Johns Hopkins University (US), University of Massachusetts Chan Medical School (US), Shanghai Jiao Tong University (CN), Anhui Medical University (CN), Johns Hopkins Medicine (US), Shanghai Ninth People's Hospital (CN), Southern California Eye Institute (US), United Nations University-International Institute for Global Health (MY), Microsoft Research Asia (China) (CN), Tsinghua–Berkeley Shenzhen Institute (CN), First Affiliated Hospital of Anhui Medical University (CN), Second Affiliated Hospital of Zhejiang University (CN), Xi’an Jiaotong-Liverpool University (CN), Halmstad University (SE), Hainan Medical University (CN), Aalborg University (DK), Tsinghua University (CN)
Research to Prevent Blindness, Shanghai Municipal Health Commission, National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Shanghai Jiao Tong University, Shanghai Municipal Health and Family Planning Commission, National Key Research and Development Program of China
Reduced inequalities
Openalex Percentile: Top 6%
Genomics and Rare Diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.