Evidence map›Paper›PMID 42130943›Full record

ReviewPatterns (New York, N.Y.)2026

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

Chaoyu Lei, Ying Zuo, Claudia Abreu Lopes, Jaimee Stuart, Benjamin Xu, T Y Alvin Liu, Thomas Ploug, Zilong Wang, Kang Dang, Kai Jin and 6 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Chaoyu LeiState Key Laboratory of Eye Health, Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ying ZuoSchool of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China.
Claudia Abreu LopesUnited Nations University International Institute for Global Health, Kuala Lumpur, Malaysia.
Jaimee StuartUnited Nations University Institute in Macau, Macau SAR, China.
Benjamin XuKeck School of Medicine, Roski Eye Institute, University of Southern California, Los Angeles, CA, USA.
T Y Alvin LiuWilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Thomas PlougCentre for AI Ethics, Law, and Policy, Department of Communication and Psychology, Aalborg University, Copenhagen, Denmark.
Zilong WangMicrosoft Research Asia, Shanghai, China.
Kang DangSchool of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China.
Kai JinEye Center of Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Haoxuan YuUnited Nations University International Institute for Global Health, Kuala Lumpur, Malaysia.
Heaven Yeshaneh TatereUMass Chan Medical School, Worcester, MA, USA.
Fanyi KongInstitute for Hospital Management of Tsinghua University, Shenzhen, Guangdong, China.
Ning ZhangDepartment of Obstetrics and Gynecology, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Lufa ZhangSchool of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China.
Huifang ZhouState Key Laboratory of Eye Health, Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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.

Indexed as

artificial intelligenceethicsfairnesshealth equityrare disease

Identifiers

PMID42130943
PMCPMC13161689

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.