{"doi":"10.1093/jamia/ocab098","title":"The application of artificial intelligence and data integration in COVID-19 studies: a scoping review","abstract":"OBJECTIVE: To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. MATERIALS AND METHODS: We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. RESULTS: In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. DISCUSSION: Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. CONCLUSION: There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.","journal":"Journal of the American Medical Informatics Association","year":2021,"id":162137,"datarank":1.8678851032200166,"base_score":3.9318256327243257,"endowment":3.9318256327243257,"self_citation_contribution":0.5897738449086489,"citation_network_contribution":1.2781112583113676,"self_endowment_contribution":0.5897738449086489,"citer_contribution":1.2781112583113676,"corpus_percentile":88.65939506459348,"corpus_rank":1467,"citation_count":50,"citer_count":50,"citers_with_citation_signal":35,"citers_with_endowment":35,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7692,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":4.1667,"fair_percentile":4.891470498318557,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":330065,"name":"Yahan Zhang","orcid":null,"position":1,"is_corresponding":false},{"id":285592,"name":"Tianchen Lyu","orcid":"0000-0002-0981-3847","position":2,"is_corresponding":false},{"id":285001,"name":"Mattia Prosperi","orcid":"0000-0002-9021-5595","position":3,"is_corresponding":false},{"id":240709,"name":"Fei Wang","orcid":"0000-0001-9459-9461","position":4,"is_corresponding":false},{"id":12534,"name":"Hua Xu","orcid":"0000-0002-5274-4672","position":5,"is_corresponding":false},{"id":23318,"name":"Jiang Bian","orcid":"0000-0002-2238-5429","position":6,"is_corresponding":false},{"id":263949,"name":"Yi Guo","orcid":"0000-0003-0587-4105","position":0,"is_corresponding":true}],"reference_count":168,"raw_metadata":null,"created_at":"2026-07-18T23:45:00.600065Z","pmid":"34151987","pmcid":"PMC8344463","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":0.0,"fair_a":0.0,"fair_i":20.0,"fair_r":8.3333,"fair_zscore":-1.1986,"fair_rationale":{"fair_score":4.17,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":0.0,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"No new data were generated in support of this research.","grounded":true,"rationale":"The paper declares that no new data were generated, so there is no dataset to identify with a persistent identifier.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a 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[majority verdict 'no' (4/5 passes agreed)]","anchors":["RDA-I3-01M — '(meta)data include references to other (meta)data'","RDA-I3-03M — 'metadata includes qualified references to other metadata'","FsF-I3-01M — F-UJI: 'Metadata includes links between the data and its related entities'"],"scored":false,"signal":null}]},"R":{"name":"Reusable","score":8.33,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"No new data were generated in support of this research.","grounded":true,"rationale":"No reuse license is attached to the data because no data were generated.","anchors":["RDA-R1.1-01M — 'Metadata includes information about the licence under which the data can be reu","RDA-R1.1-02M — 'Metadata refers to a standard reuse licence'","RDA-R1.1-03M — 'Metadata refers to a machine-understandable reuse licence'"],"scored":true,"signal":null},{"key":"r_provenance_methods","label":"Provenance of the data","kind":"llm","weight":1.0,"fraction":0.0,"verdict":"no","evidence":"No new data were generated in support of this research.","grounded":true,"rationale":"No production record is given because no data were produced. 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For proteomics data, deposit in PRIDE (PXD accession) or ProteomeXchange.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"The paper declares that no new data were generated, so there is no dataset to identify with a persistent identifier.","gain":16.67,"priority":"essential","scored":true},{"key":"f_repository_named","dimension":"F","label":"Named repository","action":"Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For proteomics data, deposit in PRIDE (PXD accession) or ProteomeXchange.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"Since no new data were generated, no repository is named as the holder.","gain":16.67,"priority":"essential","scored":true},{"key":"a_data_openly_accessible","dimension":"A","label":"Access route free of preconditions","action":"Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. 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Prefer open proteomics formats such as mzML or mzIdentML.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"No file format is named for the data because no data were generated.","gain":8.33,"priority":"important","scored":true},{"key":"x_code_availability","dimension":"R","label":"Analysis code available","action":"Publish the analysis code in a public forge, archive a tagged release with a DOI (Zenodo/Software Heritage), and cite that DOI in the paper. NIH DMS Element 2 asks for the tools and code, not only the data — and 'available on request' is not a locator. Archive the analysis code in a versioned repository (GitHub + a Zenodo release DOI).","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"The paper does not mention any code written for this review; no code locator is provided.","gain":8.33,"priority":"important","scored":true},{"key":"r_versioning","dimension":"R","label":"Snapshot identified","action":"Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"No version token or date is stated for the data because no dataset exists. [majority verdict 'no' (4/5 passes agreed)]","gain":4.17,"priority":"useful","scored":true},{"key":"f_data_availability_statement","dimension":"F","label":"Data-availability statement","action":"Replace the statement with the repository template: name the repository and give the accession or DOI (Colavizza category 3). This is the only DAS class associated with a measured citation advantage; 'available on reasonable request' and 'within the article' are not.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"The data availability statement explicitly states that no new data were generated, which is a statement of unavailability.","gain":0.0,"priority":"essential","scored":false},{"key":"f_discovery_metadata","dimension":"F","label":"Description of the dataset as an object","action":"Add a 'Data Records' section: itemise every file in the deposit and every variable or sample it holds, with counts and units. Describe the dataset as an object in its own right, not as a by-product of the findings — this is what makes it discoverable to someone who is not looking for your paper.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"No dataset exists, so no itemised inventory or description of data is provided.","gain":0.0,"priority":"essential","scored":false},{"key":"a_access_conditions_stated","dimension":"A","label":"Access level labelled","action":"State the access level in words, using the standard vocabulary: 'These data are open access' / 'These data are controlled access'. 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In proteomics, describe the data with mzML or MIAPE.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline","why":"The paper uses PRISMA, a manuscript reporting guideline, not a data or metadata standard. [majority verdict 'partial' (3/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"r_provenance_methods","dimension":"R","label":"Provenance of the data","action":"Name the instruments, kits, and software — with versions — that produced the data, not just the verbs. 'Reads were aligned' is not provenance; 'aligned with STAR v2.7.9a to GRCh38' is, because someone else can rerun it.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"No production record is given because no data were produced. [majority verdict 'no' (4/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"r_documentation_codebook","dimension":"R","label":"Documentation / codebook","action":"Ship a README and a data dictionary IN the deposit — every file, every variable, its units, its allowed values, its missing-value codes. It is the cheapest single thing that makes a dataset usable by someone who was not in the lab, and a table buried in the article does not travel with the data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"No documentation object accompanies the data because no data exist. [majority verdict 'no' (4/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"a_controlled_access_for_sensitive","dimension":"A","label":"Gatekeeper for sensitive data","action":"Route sensitive data through an institutional gatekeeper — deposit in a controlled- access repository (dbGaP, EGA) with a Data Access Committee and a published DUA — rather than through the corresponding author's inbox. 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A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"The paper is a review and does not generate data that depend on other resources; no external resource identifiers are given for the data's dependencies. [majority verdict 'no' (4/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"No new data were generated in support of this research.","why":"No persistence commitment or availability timing is stated because no data were generated.","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For proteomics data, deposit in PRIDE (PXD accession) or ProteomeXchange.","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For proteomics data, deposit in PRIDE (PXD accession) or ProteomeXchange.","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For proteomics data, deposit in PRIDE (PXD accession) or ProteomeXchange.","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the proteomics repository accession (e.g. from PRIDE (PXD accession) or ProteomeXchange) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-07-20T11:20:21.869688Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}