Evidence map›Paper›PMID 38231037›Full record

ArticleRadiology. Artificial intelligence2024

Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence.

Jefferson R Wilson, Luciano M Prevedello, Christopher D Witiw, Adam E Flanders, Errol Colak

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Data Resources: Milestones and Building Blocks.Radiology. Artificial intelligence · 2023
    Article
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

5 authors.

Jefferson R WilsonFrom the Division of Neurosurgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), St Michael's Hospital, 30 Bond St, Toronto, ON, Canada M5B 1W8; Department of Surgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), University of Toronto, Toronto, Canada (J.R.W., C.D.W.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).ORCID 0000-0001-5965-0305
Luciano M PrevedelloFrom the Division of Neurosurgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), St Michael's Hospital, 30 Bond St, Toronto, ON, Canada M5B 1W8; Department of Surgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), University of Toronto, Toronto, Canada (J.R.W., C.D.W.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).ORCID 0000-0002-6768-6452
Christopher D WitiwFrom the Division of Neurosurgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), St Michael's Hospital, 30 Bond St, Toronto, ON, Canada M5B 1W8; Department of Surgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), University of Toronto, Toronto, Canada (J.R.W., C.D.W.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).ORCID 0000-0002-3999-7632
Adam E FlandersFrom the Division of Neurosurgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), St Michael's Hospital, 30 Bond St, Toronto, ON, Canada M5B 1W8; Department of Surgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), University of Toronto, Toronto, Canada (J.R.W., C.D.W.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).ORCID 0000-0002-4679-0787
Errol ColakFrom the Division of Neurosurgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), St Michael's Hospital, 30 Bond St, Toronto, ON, Canada M5B 1W8; Department of Surgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), University of Toronto, Toronto, Canada (J.R.W., C.D.W.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).ORCID 0000-0002-3771-7975

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In spite of an exponential increase in the volume of medical data produced globally, much of these data are inaccessible to those who might best use them to develop improved health care solutions through the application of advanced analytics such as artificial intelligence. Data liberation and crowdsourcing represent two distinct but interrelated approaches to bridging existing data silos and accelerating the pace of innovation internationally. In this article, we examine these concepts in the context of medical artificial intelligence research, summarizing their potential benefits, identifying potential pitfalls, and ultimately making a case for their expanded use going forward. A practical example of a crowdsourced competition using an international medical imaging dataset is provided.

Indexed as

Biomedical ResearchCrowdsourcingHolometabolaAnimalsArtificial IntelligenceHealth FacilitiesArtificial IntelligenceCrowdsourcingData Liberation

Identifiers

PMID38231037
PMCPMC10831522

What OpenQuestion holds

Textmetadata
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.