Evidence map›Paper›PMID 41383221›Full record

ArticleNEJM AI2025

Mitigating Limited Data Challenges to Improve Artificial Intelligence Integration in Rare Disease Drug Development.

Atasi Poddar, Gabriel K Innes, Qi Liu, Anindita Saha, Morgan Hanger, Kelly Franzetti, M Khair ElZarrad, Tala H Fakhouri

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NEJM AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Atasi PoddarOffice of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.ORCID 0009-0004-1393-669X
Gabriel K InnesOffice of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0002-8206-3608
Qi LiuOffice of Translational Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0002-4053-4213
Anindita SahaOffice of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0003-0306-4264
Morgan HangerClinical Trials Transformation Initiative, Duke Clinical Research Institute, Durham, NC, USA.ORCID 0000-0003-0577-2416
Kelly FranzettiClinical Trials Transformation Initiative, Duke Clinical Research Institute, Durham, NC, USA.ORCID 0009-0007-7210-3892
M Khair ElZarradOffice of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.ORCID 0009-0005-2558-6989
Tala H FakhouriOffice of Medical Policy, U.S. Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0002-0523-3466

Funding

FDA HHS U18 FD005292Intramural FDA HHS FD999999
6 · The paper itself

Abstract

The Orphan Drug Act defines a rare disease as a condition affecting fewer than 200,000 people in the United States. However, most rare diseases are categorized as ultrarare or hyper-rare, impacting fewer than 100 individuals worldwide. Developing drugs for these conditions involves multiple challenges, such as geographically dispersed and small patient populations, limited natural history data, and poor disease characterization. Issues related to small patient numbers, scarce natural history information, and clinical heterogeneity within rare diseases can be addressed by various strategies, including using artificial intelligence and advanced analytical methods, leveraging detailed individual-level data, and exploring synthetic data generation to overcome the limitations of small datasets. Moreover, establishing centralized databases and promoting public-private partnerships can help build a more comprehensive repository of available data.

Identifiers

PMID41383221
PMCPMC12690552

What OpenQuestion holds

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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.