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A generalist vision–language foundation model for diverse biomedical tasks

Nature Medicine(2024)10.1038/s41591-024-03185-2Source: DataRank Database

A generalist vision–language foundation model for diverse biomedical tasks is a research paper published in Nature Medicine (2024). On theSindex it has a DataRank of 0.819. It has been cited 234 times.

N/A

0.819DataRank · unranked

Scored on demand from live citation data

Open Access234 citations

Cite:

Linked data & code

BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks · arXiv

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (31 grants)Impact (FWCI 36.79)OA: greenTopics (24)Linked Datasets (1)DataCite Reuse (1)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.819

From this paper's citation signal

Citation Network Contribution

0

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

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Authors (23)

Rong Zhou ORCID,Eashan Adhikarla ORCID,Zhiling Yan ORCID,Yixin Liu ORCID,Jun Yu ORCID

Live enrichment

PubMed:39112796 PMC:PMC12581140 OpenAlex:https://openalex.org/W4401397240

National Science Foundation

Grant: CRII-2246067

National Science Foundation

Grant: POSE: Phase II-2346158

National Science Foundation

Grant: NCS-2319451

National Science Foundation

Grant: MRI-2215789

National Science Foundation

Grant: IIS-1909879

National Science Foundation

Grant: NCS-2319451, MRI-2215789 and IIS-1909879

Lehigh University

Grant: FRGS00011497

Lehigh University

Grant: S00010293

Lehigh University

Grant: S001250

U.S. Department of Health & Human Services | National Institutes of Health

Grant: R21EY034179

U.S. Department of Health & Human Services | National Institutes of Health

Grant: R01EB19403

U.S. Department of Health & Human Services | National Institutes of Health

Grant: R01LM11934

U.S. Department of Health & Human Services | National Institutes of Health

Grant: R03AG078625

U.S. Department of Health & Human Services | National Institutes of Health

Grant: R01HL159183

U.S. Department of Health & Human Services | National Institutes of Health

Grant: RF1AG057892

U.S. Department of Health & Human Services | National Institutes of Health

Grant: R01EB19403 and R01LM11934

NHGRI NIH HHS

Grant: R01 HG012748

NICHD NIH HHS

Grant: R01 HD099348

NLM NIH HHS

Grant: R01 LM011934

NIBIB NIH HHS

Grant: R01 EB019403

National Institutes of Health

Grant: 1R03AG078625-01

Identification of Multi-modal Imaging Biomarkers for Early Prediction of MCI-AD Conversion via Multigraph Representation

National Science Foundation

Grant: 2246067

CRII: III: Towards Improving the Handling of Heterogeneity and Personalization in Federated Learning

National Institutes of Health

Grant: 1R21EY034179-01A1

Surrogate Augmented Deep Predictive Learning for Retinopathy of Prematurity

National Science Foundation

Grant: 1909879

RI: Small: A Study of Agent's Expectations for Nondeterministic and Dynamic Domains

National Science Foundation

Grant: 2215789

MRI: Development of Heterogeneous Edge Computing Platform for Real-Time Scientific Machine Learning

National Institutes of Health

Grant: 2R01EB019403-05

Secondary use of EMRs for surgical complication surveillance

National Institutes of Health

Grant: 1RF1AG057892-01A1

FiberNET: Deep learning to evaluate brain tract integrity worldwide and in AD

National Science Foundation

Grant: 2346158

NSF POSE: Phase II: OpenAD: An Integrated Open-Source Ecosystem for Anomaly Detection

National Science Foundation

Grant: 2319451

Collaborative Research: NCS-FO: Dynamic Brain Graph Mining

National Institutes of Health

Grant: 1R01HL159183-01A1

Deep learning Based Phenotyping and Treatment Optimization for Heart Failure with Preserved Ejection Fraction

National Institutes of Health

Grant: 2R01LM011934-06A1

Semi-structured Information Retrieval in Clinical Text for Cohort Identification

FWCI

36.79

Citation Percentile

1.0%

Citation Trend

2024

2025

2026

greenLicense: Springer Nature TDM

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12581140/pdf/nihms-2114690.pdf

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12581140/pdf/nihms-2114690.pdf

publisherhttps://www.nature.com/articles/s41591-024-03185-2.pdf

publisherhttps://www.nature.com/articles/s41591-024-03185-2

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/12581140

Fields of Study

Multimodal Machine Learning ApplicationsTopic ModelingDomain Adaptation and Few-Shot Learning0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

MeSH Terms

Artificial IntelligenceHumansRadiology

Keywords

Generalist and specialist speciesFoundation (evidence)Cognitive scienceComputer scienceMedicineArtificial intelligencePsychologyEngineering ethicsBiologyEcologyEngineeringGeographyArchaeologyHumansRadiologyArticle

BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks

10.48550/arxiv.2305.17100arXiv

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