흉부 영상의학에서 인공지능의 현재와 미래: 폐질환 진단의 새로운 패러다임
Purpose: This review explores the current applications and future prospects of artificial intelligence (AI) in thoracic imaging, with a particular focus on chest radiography (chest X-ray, CXR) and computed tomography (CT). Current Concepts: Recently developed CXR AI algorithms have improved the effi...
| Publicado en: | Journal of the Korean Medical Association / Taehan Uisa Hyophoe Chi Vol. 68; no. 5; pp. 288 - 301 |
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| Autor principal: | |
| Formato: | diagnostic images review tables/charts Journal Article |
| Publicado: |
Korean Medical Association
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185832823&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185832823 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19758456 B9M7 jtl: Journal of the Korean Medical Association / Taehan Uisa Hyophoe Chi issn: 19758456 maglogo: N pubinfo: dt: May2025 vid: 68 iid: 5 pid: 64891 pub: Korean Medical Association place: , <Blank> artinfo: ui: 185832823 185832823 185832823 10.5124/jkma.25.0054 185832823 ppf: 288 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 흉부 영상의학에서 인공지능의 현재와 미래: 폐질환 진단의 새로운 패러다임 aug: au: Jin, Gong Yong affil: Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea sug: subj: Artificial Intelligence Utilization Lung Diseases Diagnosis Radiography, Thoracic Tomography, X-Ray Computed Algorithms Sensitivity and Specificity Workflow Lung Neoplasms Diagnosis Image Interpretation, Computer Assisted ab: Purpose: This review explores the current applications and future prospects of artificial intelligence (AI) in thoracic imaging, with a particular focus on chest radiography (chest X-ray, CXR) and computed tomography (CT). Current Concepts: Recently developed CXR AI algorithms have improved the efficiency, accuracy, and consistency of radiologists' routine clinical workflows by assisting in the detection of a wide range of thoracic diseases on CXR. These AI systems demonstrate diagnostic performance comparable to that of radiology residents who have limited interpretive experience. Furthermore, generative CXR AI technologies are capable of not only automatically detecting abnormalities such as pulmonary nodules, pneumonia, pneumothorax, and tuberculosis, but also generating radiology reports. These advancements represent a paradigm-shifting innovation that may significantly alter the current landscape of CXR interpretation in thoracic radiology. Although performance varies depending on the specific algorithm and dataset, AI applied to low-dose chest CT has demonstrated diagnostic accuracy ranging from 0.81 to 0.98 for nodule detection and malignancy assessment, with sensitivity ranging from 0.88 to 0.99 and specificity from 0.82 to 0.93. Incorporating AI as a second reader in CT interpretation can reduce reading time by approximately 20%, while also improving sensitivity for pulmonary nodule detection by 5% to 20% and malignant nodule diagnosis by 3% to 15%. Discussion and Conclusion: Both CXR AI and chest CT AI streamline image interpretation by assisting with simple and repetitive tasks. Simultaneously, they provide novel diagnostic insights that are expected to influence and potentially reshape the interpretative patterns of radiologists in the near future. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: Korean refInfo: holdings: @attributes: islocal: N |
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