Evidence map›Paper›PMID 40714006›Full record

ReviewCell reports methods2025

Recent advances and future prospects for blockchain in biomedicine.

Eric Ni, Xiangru Tang, Xiao Zhou, Danielle Lee, Ahmed Elhussein, Elizabeth Knight, Gamze Gürsoy, Mark Gerstein

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cell reports methods, 2025. 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

8 authors.

Eric NiProgram in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA.
Xiangru TangDepartment of Computer Science, Yale University, New Haven, CT, USA.
Xiao ZhouProgram in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA.
Danielle LeeDepartment of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA.
Ahmed ElhusseinDepartment of Biomedical Informatics & Data Science, Yale University, New Haven, CT, USA.
Elizabeth KnightSchool of Medicine, Yale University, New Haven, CT, USA.
Gamze GürsoyDepartment of Biomedical Informatics, Columbia University, New York City, NY, USA; New York Genome Center, New York City, NY, USA; Department of Computer Science, Columbia University, New York City, NY, USA.
Mark GersteinProgram in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA; Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT, USA; Department of Statistics & Data Science, Yale University, New Haven, CT, USA. Electronic address: mark@gersteinlab.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare data are rapidly evolving with the introduction of new modalities and an exponential increase in volume. Current health data storage and communication services face major obstacles in terms of privacy, security, and operational efficiency in the face of this new data landscape. Blockchain technology, characterized by its immutability, auditability, and decentralization, is emerging as a promising solution. However, integrating blockchain into existing systems presents significant challenges, particularly regarding data privacy and scalability. This review aims to provide a comprehensive understanding of how blockchain technology can transform the biomedical sector, potentially making healthcare data management more secure and efficient.

Indexed as

BlockchainComputer SecurityHumansInformation Storage and RetrievalbiomedicineblockchainCP: biotechnologyCP: computational biologydatabaseshealthcare

Identifiers

PMID40714006
PMCPMC12461581

What OpenQuestion holds

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LicenceCC BY-NC
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.