Evidence map›Paper›PMID 34870224›Full record

ArticleRadiology. Artificial intelligence2021

Imaging AI in Practice: A Demonstration of Future Workflow Using Integration Standards.

Walter F Wiggins, Kirti Magudia, Teri M Sippel Schmidt, Stacy D O'Connor, Christopher D Carr, Marc D Kohli, Katherine P Andriole

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
–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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
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  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. A Cloud-Based System for Automated AI Image Analysis and Reporting.Journal of imaging informatics in medicine · 2025
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  11. Review
  12. Article
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  14. Segmentation and Volumetric Analysis of Heart from Cardiac CT Images.Cardiovascular engineering and technology · 2024
    Article
  15. Article
  16. Review
  17. Review
  18. Model guided medicine and the search for truth.International journal of computer assisted radiology and surgery · 2024
    Article
  19. Article
  20. 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

7 authors.

Walter F Wiggins *Department of Radiology, Duke University School of Medicine, DUMC Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, Calif (K.M., M.D.K.); Department of Biomedical Engineering, Marquette University, Milwaukee, Wis (T.M.S.S.); Departments of Biomedical Engineering (T.M.S.S.) and Radiology (S.D.O.), Medical College of Wisconsin, Milwaukee, Wis; Department of Informatics, Radiological Society of North America, Oak Brook, Ill (C.D.C.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (K.P.A.); and Mass General Brigham Center for Clinical Data Science, Boston, Mass (K.P.A.).ORCID https://orcid.org/0000-0002-0258-2708
Kirti Magudia *Department of Radiology, Duke University School of Medicine, DUMC Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, Calif (K.M., M.D.K.); Department of Biomedical Engineering, Marquette University, Milwaukee, Wis (T.M.S.S.); Departments of Biomedical Engineering (T.M.S.S.) and Radiology (S.D.O.), Medical College of Wisconsin, Milwaukee, Wis; Department of Informatics, Radiological Society of North America, Oak Brook, Ill (C.D.C.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (K.P.A.); and Mass General Brigham Center for Clinical Data Science, Boston, Mass (K.P.A.).ORCID https://orcid.org/0000-0001-7037-433X
Teri M Sippel SchmidtDepartment of Radiology, Duke University School of Medicine, DUMC Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, Calif (K.M., M.D.K.); Department of Biomedical Engineering, Marquette University, Milwaukee, Wis (T.M.S.S.); Departments of Biomedical Engineering (T.M.S.S.) and Radiology (S.D.O.), Medical College of Wisconsin, Milwaukee, Wis; Department of Informatics, Radiological Society of North America, Oak Brook, Ill (C.D.C.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (K.P.A.); and Mass General Brigham Center for Clinical Data Science, Boston, Mass (K.P.A.).
Stacy D O'ConnorDepartment of Radiology, Duke University School of Medicine, DUMC Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, Calif (K.M., M.D.K.); Department of Biomedical Engineering, Marquette University, Milwaukee, Wis (T.M.S.S.); Departments of Biomedical Engineering (T.M.S.S.) and Radiology (S.D.O.), Medical College of Wisconsin, Milwaukee, Wis; Department of Informatics, Radiological Society of North America, Oak Brook, Ill (C.D.C.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (K.P.A.); and Mass General Brigham Center for Clinical Data Science, Boston, Mass (K.P.A.).ORCID https://orcid.org/0000-0002-0813-1414
Christopher D CarrDepartment of Radiology, Duke University School of Medicine, DUMC Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, Calif (K.M., M.D.K.); Department of Biomedical Engineering, Marquette University, Milwaukee, Wis (T.M.S.S.); Departments of Biomedical Engineering (T.M.S.S.) and Radiology (S.D.O.), Medical College of Wisconsin, Milwaukee, Wis; Department of Informatics, Radiological Society of North America, Oak Brook, Ill (C.D.C.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (K.P.A.); and Mass General Brigham Center for Clinical Data Science, Boston, Mass (K.P.A.).
Marc D KohliDepartment of Radiology, Duke University School of Medicine, DUMC Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, Calif (K.M., M.D.K.); Department of Biomedical Engineering, Marquette University, Milwaukee, Wis (T.M.S.S.); Departments of Biomedical Engineering (T.M.S.S.) and Radiology (S.D.O.), Medical College of Wisconsin, Milwaukee, Wis; Department of Informatics, Radiological Society of North America, Oak Brook, Ill (C.D.C.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (K.P.A.); and Mass General Brigham Center for Clinical Data Science, Boston, Mass (K.P.A.).ORCID 0000-0003-3720-0156
Katherine P AndrioleDepartment of Radiology, Duke University School of Medicine, DUMC Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, Calif (K.M., M.D.K.); Department of Biomedical Engineering, Marquette University, Milwaukee, Wis (T.M.S.S.); Departments of Biomedical Engineering (T.M.S.S.) and Radiology (S.D.O.), Medical College of Wisconsin, Milwaukee, Wis; Department of Informatics, Radiological Society of North America, Oak Brook, Ill (C.D.C.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (K.P.A.); and Mass General Brigham Center for Clinical Data Science, Boston, Mass (K.P.A.).ORCID https://orcid.org/0000-0001-7774-4225

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools.

Indexed as

Computer Applications-General (Informatics)Technology Assessment

Identifiers

PMID34870224
PMCPMC8637229

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.