Evidence map›Paper›PMID 38197795›Full record

ArticleRadiology. Artificial intelligence2024

AI for Detection of Tuberculosis: Implications for Global Health.

Eui Jin Hwang, Won Gi Jeong, Pierre-Marie David, Matthew Arentz, Morten Ruhwald, Soon Ho Yoon

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

Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
10.9field-weighted citation impact, top 1% 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

17 citing papers in PubMed, 27 citations in OpenAlex.

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  16. Celebrating the Journal's First 5 Years.Radiology. Artificial intelligence · 2024
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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

6 authors at 3 institutions in 3 countries.

Eui Jin HwangFrom the Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy, University of Montréal, Montréal, Canada (P.M.D.); OBVIA-Observatoire sur les Impacts Sociétaux de l'IA et du Numérique, Québec, Canada (P.M.D.); and FIND-The Global Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.).ORCID 0000-0002-3697-5542
Won Gi JeongFrom the Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy, University of Montréal, Montréal, Canada (P.M.D.); OBVIA-Observatoire sur les Impacts Sociétaux de l'IA et du Numérique, Québec, Canada (P.M.D.); and FIND-The Global Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.).ORCID 0000-0003-2821-2788
Pierre-Marie DavidFrom the Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy, University of Montréal, Montréal, Canada (P.M.D.); OBVIA-Observatoire sur les Impacts Sociétaux de l'IA et du Numérique, Québec, Canada (P.M.D.); and FIND-The Global Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.).ORCID 0000-0001-7441-0633
Matthew ArentzFrom the Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy, University of Montréal, Montréal, Canada (P.M.D.); OBVIA-Observatoire sur les Impacts Sociétaux de l'IA et du Numérique, Québec, Canada (P.M.D.); and FIND-The Global Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.).
Morten RuhwaldFrom the Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy, University of Montréal, Montréal, Canada (P.M.D.); OBVIA-Observatoire sur les Impacts Sociétaux de l'IA et du Numérique, Québec, Canada (P.M.D.); and FIND-The Global Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.).
Soon Ho YoonFrom the Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy, University of Montréal, Montréal, Canada (P.M.D.); OBVIA-Observatoire sur les Impacts Sociétaux de l'IA et du Numérique, Québec, Canada (P.M.D.); and FIND-The Global Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.).ORCID 0000-0002-3700-0165
Chonnam National University Hwasun Hospital · KRFIND · CHSeoul National University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity.

Indexed as

Artificial IntelligenceTuberculosisDiagnosis, Computer-AssistedGlobal HealthHumansSoftwareComputer-aided Diagnosis (CAD)Conventional RadiographyLungMachine LearningThorax

Identifiers

PMID38197795
PMCPMC10982823
OpenAlexW4390704971

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.