Evidence map›Paper›PMID 36721409›Full record

ArticleRadiology. Artificial intelligence2023

Artificial Intelligence and Radiology Education.

Ali S Tejani, Hesham Elhalawani, Linda Moy, Marc Kohli, Charles E Kahn

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 3 pooled it
3.3field-weighted citation impact, top 8% of its field
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

35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 59 citations in OpenAlex.

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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 at 5 institutions in 1 country.

Ali S TejaniDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiation Oncology, Brigham and Women's Hospital, Boston, Mass (H.E.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology, University of California San Francisco, San Francisco, Calif (M.K.); and Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.).ORCID https://orcid.org/0000-0002-6862-5299
Hesham ElhalawaniDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiation Oncology, Brigham and Women's Hospital, Boston, Mass (H.E.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology, University of California San Francisco, San Francisco, Calif (M.K.); and Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.).
Linda MoyDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiation Oncology, Brigham and Women's Hospital, Boston, Mass (H.E.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology, University of California San Francisco, San Francisco, Calif (M.K.); and Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.).ORCID https://orcid.org/0000-0001-9564-9360
Marc KohliDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiation Oncology, Brigham and Women's Hospital, Boston, Mass (H.E.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology, University of California San Francisco, San Francisco, Calif (M.K.); and Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.).ORCID 0000-0003-3720-0156
Charles E KahnDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiation Oncology, Brigham and Women's Hospital, Boston, Mass (H.E.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology, University of California San Francisco, San Francisco, Calif (M.K.); and Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.).ORCID https://orcid.org/0000-0002-6654-7434
Brigham and Women's Hospital · USNew York University · USThe University of Texas Southwestern Medical Center · USUniversity of California, San Francisco · USUniversity of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course - Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of "AI and radiology education" includes AI-augmented radiology education, with emphasis on the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022

Indexed as

Artificial IntelligenceImaging InformaticsImpact of AI on EducationMedical EducationNatural Language ProcessingPrecision EducationUse of AI in Education

Identifiers

PMID36721409
PMCPMC9885376
OpenAlexW4309322815

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.