Successful Implementation of an Artificial Intelligence-Based Computer-Aided Detection System for Chest Radiography in Daily Clinical Practice
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The CAD-first model decreased physicians' workloads by limiting the number of CXRs for them to interpret to those rated as TB-presumptive.
“The CAD-first model decreased physicians’ workloads by limiting the number of CXRs for them to interpret to those rated as TB-presumptive; programmatic implementation in other settings has shown similar benefits. (34, 39)”
From Active case finding to detect symptomatic and subclinical pulmonary tuberculosis disease: implementation of computer-aided detection for chest radiography in Viet Nam (2024) · cites this paper as [39]
The Take-home points (page not provided) describe AI-CAD improving radiologists’ workflow by prioritizing chest radiographs according to predicted abnormalities. They do not describe a CAD-first TB screening model or say that physicians interpreted only TB-presumptive radiographs; the paper instead discusses prioritizing formal readings. (LLM, conf=0.84)
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References 23 · Cited by 31
Research works printed but not linked: 22
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- [5]van Ginneken B Hogeweg L Prokop M Computer-aided diagnosis in chest radiography: beyond nodules Eur J Radiol 2009 72 226 230 19604661
- [6]Edwards M Lawson Z Morris S Evans A Harrison S Isaac R The presence of radiological features on chest radiographs: how well do clinicians agree? Clin Radiol 2012 67 664 668 22342102
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- [9]Coppola F Faggioni L Regge D Giovagnoni A Golfieri R Bibbolino C Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey Radiol Med 2021 126 63 71 32350797
- [10]Kulkarni S Seneviratne N Baig MS Khan AHA Artificial intelligence in medicine: where are we now? Acad Radiol 2020 27 62 70 31636002
- [11]Chassagnon G Vakalopoulou M Paragios N Revel MP Artificial intelligence applications for thoracic imaging Eur J Radiol 2020 123 108774 31841881
- [12]van Leeuwen KG Schalekamp S Rutten MJCM van Ginneken B de Rooij M Artificial intelligence in radiology: 100 commercially available products and their scientific evidence Eur Radiol 2021 31 3797 3804 33856519 PMC8128724
- [13]Jin KN Kim EY Kim YJ Lee GP Kim H Oh S Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study Eur Radiol 2022 32 3469 3479 34973101 PMC9038825
- [14]Hwang EJ Kim H Yoon SH Goo JM Park CM Implementation of a deep learning-based computer-aided detection system for the interpretation of chest radiographs in patients suspected for COVID-19 Korean J Radiol 2020 21 1150 1160 32729263 PMC7458860
- [15]van Leeuwen KG de Rooij M Schalekamp S van Ginneken B Rutten MJCM How does artificial intelligence in radiology improve efficiency and health outcomes? Pediatr Radiol 2021 06 10.1007/s00247-021-05114-8 [Epub] PMC9537124 34117522 · DOI · Paper page
- [16]Kim SJ Roh JW Kim S Park JY Choi D Current state and strategy for establishing a digitally innovative hospital: memorial review article for opening of Yongin Severance Hospital Yonsei Med J 2020 61 647 651 32734727 PMC7393291
- [17]Lee JH Sun HY Park S Kim H Hwang EJ Goo JM Performance of a deep learning algorithm compared with radiologic interpretation for lung cancer detection on chest radiographs in a health screening population Radiology 2020 297 687 696 32960729
- [19]Hwang EJ Park S Jin KN Kim JI Choi SY Lee JH Development and validation of a deep learning–based automated detection algorithm for major thoracic diseases on chest radiographs JAMA Netw Open 2019 2 e191095 30901052 PMC6583308
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- Authors
- Lee, S., Shin, H. J., Kim, S., & Kim, E.-K.
- Affiliations
- Yonsei University (KR)
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
- Corresponding author
- Kim, Eun-Kyung
- Venue
- Korean Journal of Radiology
- Publisher
- XMLink
- Published
- 2022
- Volume
- 23
- Issue
- 9
- Pages
- 847
- ISSN
- 1229-6929 (ISSN-L)
- 2005-8330
- PMC
- PMC9434734
- OpenAlex
- W4283692884
- Cited by
- 31
- References
- 23
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 9 · 2023: 15 · 2022: 1
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Korean Journal of Radiology (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- computer-aided detection, artificial intelligence, chest radiography, clinical implementation, lung nodule detection
- MeSH terms
- Artificial Intelligence, Computers, Humans, Radiography, Radiography, Thoracic
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