Evidence map›Paper›PMID 39436298›Full record

ArticleRadiology2024

Evaluating the Performance and Bias of Natural Language Processing Tools in Labeling Chest Radiograph Reports.

Samantha M Santomartino, John R Zech, Kent Hall, Jean Jeudy, Vishwa Parekh, Paul H Yi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

6 authors.

Samantha M SantomartinoFrom Drexel University College of Medicine, Philadelphia, Pa (S.M.S.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (J.R.Z.); Department of Radiology, Wake Forest University Health Sciences Center, Winston-Salem, NC (K.H.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (J.J., V.P.); and Department of Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Plc, Memphis, TN 38105-3678 (P.H.Y.).ORCID 0000-0001-5229-2576
John R ZechFrom Drexel University College of Medicine, Philadelphia, Pa (S.M.S.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (J.R.Z.); Department of Radiology, Wake Forest University Health Sciences Center, Winston-Salem, NC (K.H.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (J.J., V.P.); and Department of Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Plc, Memphis, TN 38105-3678 (P.H.Y.).ORCID 0000-0003-1317-8951
Kent HallFrom Drexel University College of Medicine, Philadelphia, Pa (S.M.S.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (J.R.Z.); Department of Radiology, Wake Forest University Health Sciences Center, Winston-Salem, NC (K.H.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (J.J., V.P.); and Department of Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Plc, Memphis, TN 38105-3678 (P.H.Y.).
Jean JeudyFrom Drexel University College of Medicine, Philadelphia, Pa (S.M.S.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (J.R.Z.); Department of Radiology, Wake Forest University Health Sciences Center, Winston-Salem, NC (K.H.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (J.J., V.P.); and Department of Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Plc, Memphis, TN 38105-3678 (P.H.Y.).ORCID 0000-0002-6028-459X
Vishwa ParekhFrom Drexel University College of Medicine, Philadelphia, Pa (S.M.S.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (J.R.Z.); Department of Radiology, Wake Forest University Health Sciences Center, Winston-Salem, NC (K.H.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (J.J., V.P.); and Department of Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Plc, Memphis, TN 38105-3678 (P.H.Y.).
Paul H YiFrom Drexel University College of Medicine, Philadelphia, Pa (S.M.S.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (J.R.Z.); Department of Radiology, Wake Forest University Health Sciences Center, Winston-Salem, NC (K.H.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (J.J., V.P.); and Department of Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Plc, Memphis, TN 38105-3678 (P.H.Y.).ORCID 0000-0001-9433-8093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Natural language processing (NLP) is commonly used to annotate radiology datasets for training deep learning (DL) models. However, the accuracy and potential biases of these NLP methods have not been thoroughly investigated, particularly across different demographic groups. Purpose To evaluate the accuracy and demographic bias of four NLP radiology report labeling tools on two chest radiograph datasets. Materials and Methods This retrospective study, performed between April 2022 and April 2024, evaluated chest radiograph report labeling using four NLP tools (CheXpert [rule-based], RadReportAnnotator [RRA; DL-based], OpenAI's GPT-4 [DL-based], cTAKES [hybrid]) on a subset of the Medical Information Mart for Intensive Care (MIMIC) chest radiograph dataset balanced for representation of age, sex, and race and ethnicity (

Indexed as

Natural Language ProcessingRadiography, ThoracicAdultAgedBiasDeep LearningFemaleHumansMaleMiddle AgedRetrospective Studies

Identifiers

PMID39436298
PMCPMC11535863

What OpenQuestion holds

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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.