Evidence map›Paper›PMID 37104124›Full record

ArticleRadiographics : a review publication of the Radiological Society of North America, Inc2023

Translating AI to Clinical Practice: Overcoming Data Shift with Explainability.

Youngwon Choi, Wenxi Yu, Mahesh B Nagarajan, Pangyu Teng, Jonathan G Goldin, Steven S Raman, Dieter R Enzmann, Grace Hyun J Kim, Matthew S Brown

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiographics : a review publication of the Radiological Society of North America, Inc, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  16. AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025
    Review
  17. Review
  18. Article
  19. Based on Medicine, The Now and Future of Large Language Models.Cellular and molecular bioengineering · 2024
    Review
  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

9 authors.

Youngwon ChoiFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).ORCID 0000-0003-1120-6218
Wenxi YuFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).ORCID 0000-0003-4893-9618
Mahesh B NagarajanFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).
Pangyu TengFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).
Jonathan G GoldinFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).
Steven S RamanFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).ORCID 0000-0003-0499-1676
Dieter R EnzmannFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).ORCID 0000-0002-6051-5482
Grace Hyun J KimFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).ORCID 0000-0003-1225-3489
Matthew S BrownFrom the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California-Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).ORCID 0000-0001-6805-8151

Funding

UCLA SPORE in Brain CancerP50CA211015 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI David A. Nathanson · 2017 to 2026
$25.2M
Automated Diagnosis and Progression Rate of IPF Using HRCTR21HL140465 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KIM, GRACE HYUN JUNG · 2018 to 2019
$226k
NCI NIH HHS P50 CA211015NHLBI NIH HHS R21 HL140465
6 · The paper itself

Abstract

To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main obstacles to generalizability is data shift, a data distribution mismatch between model training and real environments. Explainable AI techniques offer tools to detect and mitigate the data shift problem and develop reliable AI for clinical practice. Most medical AI is trained with datasets gathered from limited environments, such as restricted disease populations and center-dependent acquisition conditions. The data shift that commonly exists in the limited training set often causes a significant performance decrease in the deployment environment. To develop a medical application, it is important to detect potential data shift and its impact on clinical translation. During AI training stages, from premodel analysis to in-model and post hoc explanations, explainability can play a key role in detecting model susceptibility to data shift, which is otherwise hidden because the test data have the same biased distribution as the training data. Performance-based model assessments cannot effectively distinguish the model overfitting to training data bias without enriched test sets from external environments. In the absence of such external data, explainability techniques can aid in translating AI to clinical practice as a tool to detect and mitigate potential failures due to data shift.

Indexed as

AlgorithmsArtificial IntelligenceHumans

Identifiers

PMID37104124
PMCPMC10190133

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.