Evidence map›Paper›PMID 37390881›Full record

ArticleJournal of the American College of Radiology : JACR2023

Impact of Artificial Intelligence-Assisted Indication Selection on Appropriateness Order Scoring for Imaging Clinical Decision Support.

Lauren A Shreve, Jessica G Fried, Fang Liu, Quy Cao, Jina Pakpoor, Charles E Kahn, Hanna M Zafar

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Journal of the American College of Radiology : JACR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Practical applications of AI in body imaging.Abdominal radiology (New York) · 2026
    Review
  3. Review
  4. The Effect of AI on the Radiologist Workforce: A Task-Based Analysis.medRxiv : the preprint server for health sciences · 2025
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Lauren A ShreveDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: lauren.shreve@pennmedicine.upenn.edu.
Jessica G FriedProgram Director, Abdominal Imaging, Associate Medical Director of Radiology Informatics, and Co-Director, Tumor Response Assessment Core, Department of Radiology, University of Michigan, Ann Arbor, Michigan.
Fang LiuDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
Quy CaoDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
Jina PakpoorCentre for Medical Imaging, University College London, London, United Kingdom.
Charles E KahnVice Chair, Department of Radiology, and Vice Chair of Informatics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania.
Hanna M ZafarVice Chair of Quality, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania.

Funding

RESEARCH TRACK RADIOLOGY RESIDENCYT32EB004311 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Terence P Gade, Misun Hwang · 2005 to 2026
$4.5M
NIBIB NIH HHS T32 EB004311
6 · The paper itself

Abstract

purposeThe aim of this study was to assess appropriateness scoring and structured order entry after the implementation of an artificial intelligence (AI) tool for analysis of free-text indications.

methodsAdvanced outpatient imaging orders in a multicenter health care system were recorded 7 months before (March 1, 2020, to September 21, 2020) and after (October 20, 2020, to May 13, 2021) the implementation of an AI tool targeting free-text indications. Clinical decision support score (not appropriate, may be appropriate, appropriate, or unscored) and indication type (structured, free-text, both, or none) were assessed. The χ

resultsIn total, 115,079 orders before and 150,950 orders after AI tool deployment were analyzed. The mean patient age was 59.3 ± 15.5 years, and 146,035 (54.9%) were women; 49.9% of orders were for CT, 38.8% for MR, 5.9% for nuclear medicine, and 5.4% for PET. After deployment, scored orders increased to 52% from 30% (P < .001). Orders with structured indications increased to 67.3% from 34.6% (P < .001). On multivariate analysis, orders were more likely to be scored after tool deployment (odds ratio [OR], 2.7, 95% CI, 2.63-2.78; P < .001). Compared with physicians, orders placed by nonphysician providers were less likely to be scored (OR, 0.80; 95% CI, 0.78-0.83; P < .001). MR (OR, 0.84; 95% CI, 0.82-0.87) and PET (OR, 0.12; 95% CI, 0.10-0.13) were less likely to be scored than CT (; P < .001). After AI tool deployment, 72,083 orders (47.8%) remained unscored, 45,186 (62.7%) with free-text-only indications.

conclusionsEmbedding AI assistance within imaging clinical decision support was associated with increased structured indication orders and independently predicted a higher likelihood of scored orders. However, 48% of orders remained unscored, driven by both provider behavior and infrastructure-related barriers.

Indexed as

Decision Support Systems, ClinicalMedical Order Entry SystemsAdultAgedArtificial IntelligenceDiagnostic ImagingFemaleHumansMaleMiddle AgedRadionuclide Imagingartificial intelligenceImaging clinical decision supportimplementation scienceinformatics

Identifiers

PMID37390881

What OpenQuestion holds

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Registered trials

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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.