Evidence map›Paper›PMID 41495080›Full record

ArticleScientific reports2026

Utilizing AI CAD for early pandemic screening in chest radiographs.

Young Beom Kim

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Young Beom KimDepartment of Information and Communication, College of IT Engineering, Pyeongtaek University, 3825 Seodong-daero, Pyeongtaek, Gyeonggi-do, Republic of Korea. ybkim@ptu.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To investigate the potential application of existing artificial intelligence (AI) software in diagnosing COVID-19 (coronavirus disease 2019) and other pneumonia-related radiographic findings with the unprecedented challenge by COVID-19 pandemic, leveraging pre-trained commercial AI software originally designed for detecting pulmonary nodules in chest radiographs, we assessed its performance using public dataset comprising chest radiographs collected during the early stages of pandemic. The software demonstrated promising results, achieving a sensitivity of 86.83% and a specificity of 59.59% in detecting pneumonia features, including those caused by COVID-19. Considering only posterior-anterior (PA) radiographs, AI software exhibited improved performance, with sensitivity 89.16% and specificity 67.41%. The findings suggest the AI CAD software could serve as a triage for initial screening of infectious pulmonary diseases, especially in regions with limited access to advanced medical resources. This study underscores the importance of promptly adapting existing AI CAD to address urgent healthcare needs during infectious disease outbreaks.

Indexed as

Artificial IntelligenceCOVID-19Radiography, ThoracicHumansLungMass ScreeningPandemicsSARS-CoV-2Sensitivity and SpecificitySoftwareArtificial intelligenceChest radiographComputer aided detectionInfectious pulmonary diseaseInitial stage of pandemic

Identifiers

PMID41495080
PMCPMC12775148

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.