2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology.
- Published in:
- Korean Journal of Radiology, 2024, v. 25, n. 7, p. 613, doi. 10.3348/kjr.2023.1246
- By:
- Publication type:
- Article
Works by Hwang, Eui Jin
Results: 44
1
2
Caveats in Using Abnormality/Probability Scores from Artificial Intelligence Algorithms: Neither True Probability nor Level of Trustworthiness.
- Published in:
- Korean Journal of Radiology, 2024, v. 25, n. 4, p. 328, doi. 10.3348/kjr.2024.0144
- By:
- Publication type:
- Article
3
Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic.
- Published in:
- Korean Journal of Radiology, 2023, v. 24, n. 9, p. 890, doi. 10.3348/kjr.2023.0255
- By:
- Publication type:
- Article
4
Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial.
- Published in:
- Korean Journal of Radiology, 2023, v. 24, n. 3, p. 259, doi. 10.3348/kjr.2022.0651
- By:
- Publication type:
- Article
5
Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs.
- Published in:
- Korean Journal of Radiology, 2023, v. 24, n. 2, p. 155, doi. 10.3348/kjr.2022.0548
- By:
- Publication type:
- Article
6
Use of Artificial Intelligence-Based Software as Medical Devices for Chest Radiography: A Position Paper from the Korean Society of Thoracic Radiology.
- Published in:
- Korean Journal of Radiology, 2021, v. 22, n. 11, p. 1743, doi. 10.3348/kjr.2021.0544
- By:
- Publication type:
- Article
7
Implementation of a Deep Learning-Based Computer- Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.
- Published in:
- Korean Journal of Radiology, 2020, v. 21, n. 10, p. 1150, doi. 10.3348/kjr.2020.0536
- By:
- Publication type:
- Article
8
Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.
- Published in:
- Korean Journal of Radiology, 2020, v. 21, n. 5, p. 511, doi. 10.3348/kjr.2019.0821
- By:
- Publication type:
- Article
9
Quantitative Thoracic Magnetic Resonance Criteria for the Differentiation of Cysts from Solid Masses in the Anterior Mediastinum.
- Published in:
- Korean Journal of Radiology, 2019, v. 20, n. 5, p. 854, doi. 10.3348/kjr.2018.0699
- By:
- Publication type:
- Article
10
Open Bronchus Sign on CT: A Risk Factor for Hemoptysis after Percutaneous Transthoracic Biopsy.
- Published in:
- Korean Journal of Radiology, 2018, v. 19, n. 5, p. 880, doi. 10.3348/kjr.2018.19.5.880
- By:
- Publication type:
- Article
11
Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility, Learning Curves, and Reading Times.
- Published in:
- Korean Journal of Radiology, 2015, v. 16, n. 2, p. 430, doi. 10.3348/kjr.2015.16.2.430
- By:
- Publication type:
- Article
12
Monopolar Radiofrequency Ablation Using a Dual-Switching System and a Separable Clustered Electrode: Evaluation of the In Vivo Efficiency.
- Published in:
- Korean Journal of Radiology, 2014, v. 15, n. 2, p. 235, doi. 10.3348/kjr.2014.15.2.235
- By:
- Publication type:
- Article
13
Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study.
- Published in:
- 2021
- By:
- Publication type:
- journal article
14
Treatment Outcomes of Clofazimine-Containing Regimens in Severe Mycobacterium avium Complex Pulmonary Disease.
- Published in:
- Open Forum Infectious Diseases, 2024, v. 11, n. 2, p. 1, doi. 10.1093/ofid/ofad682
- By:
- Publication type:
- Article
15
Clinical Implications of Size of Cavities in Patients With Nontuberculous Mycobacterial Pulmonary Disease: A Single-Center Cohort Study.
- Published in:
- Open Forum Infectious Diseases, 2021, v. 8, n. 3, p. 1, doi. 10.1093/ofid/ofab087
- By:
- Publication type:
- Article
16
Frequency, outcome, and risk factors of contrast media extravasation in 142,651 intravenous contrast-enhanced CT scans.
- Published in:
- 2018
- By:
- Publication type:
- journal article
17
Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement.
- Published in:
- European Radiology, 2018, v. 28, n. 5, p. 2124, doi. 10.1007/s00330-017-5171-7
- By:
- Publication type:
- Article
18
Risk factors for haemoptysis after percutaneous transthoracic needle biopsies in 4,172 cases: Focusing on the effects of enlarged main pulmonary artery diameter.
- Published in:
- 2018
- By:
- Publication type:
- journal article
19
Time-dependent analysis of incidence, risk factors and clinical significance of pneumothorax after percutaneous lung biopsy.
- Published in:
- 2018
- By:
- Publication type:
- journal article
20
Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement.
- Published in:
- 2017
- By:
- Publication type:
- journal article
21
Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases.
- Published in:
- 2017
- By:
- Publication type:
- journal article
22
Retrospective assessment of interobserver agreement and accuracy in classifications and measurements in subsolid nodules with solid components less than 8mm: which window setting is better?
- Published in:
- 2017
- By:
- Publication type:
- journal article
23
Persistent part-solid nodules with solid part of 5 mm or smaller: Can the 'follow-up and surgical resection after interval growth' policy have a negative effect on patient prognosis?
- Published in:
- 2017
- By:
- Publication type:
- journal article
24
Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs.
- Published in:
- Clinical Infectious Diseases, 2019, v. 69, n. 5, p. 739, doi. 10.1093/cid/ciy967
- By:
- Publication type:
- Article
25
Improved synthesis of hyaluronic acid hydrogel and its effect on tissue augmentation.
- Published in:
- Journal of Biomaterials Applications, 2012, v. 27, n. 2, p. 179, doi. 10.1177/0885328211398508
- By:
- Publication type:
- Article
26
Portable high-intensity focused ultrasound system with 3D electronic steering, real-time cavitation monitoring, and 3D image reconstruction algorithms: a preclinical study in pigs.
- Published in:
- Ultrasonography, 2014, v. 33, n. 3, p. 191, doi. 10.14366/usg.14008
- By:
- Publication type:
- Article
27
Diagnostic performance and prognostic value of CT-defined visceral pleural invasion in early-stage lung adenocarcinomas.
- Published in:
- European Radiology, 2024, v. 34, n. 3, p. 1934, doi. 10.1007/s00330-023-10204-2
- By:
- Publication type:
- Article
28
Artificial intelligence system for identification of false-negative interpretations in chest radiographs.
- Published in:
- European Radiology, 2022, v. 32, n. 7, p. 4468, doi. 10.1007/s00330-022-08593-x
- By:
- Publication type:
- Article
29
Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial.
- Published in:
- European Radiology, 2022, v. 32, n. 1, p. 213, doi. 10.1007/s00330-021-08162-8
- By:
- Publication type:
- Article
30
Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network.
- Published in:
- 2021
- By:
- Publication type:
- journal article
31
Optimum diameter threshold for lung nodules at baseline lung cancer screening with low-dose chest CT: exploration of results from the Korean Lung Cancer Screening Project.
- Published in:
- European Radiology, 2021, v. 31, n. 9, p. 7202, doi. 10.1007/s00330-021-07827-8
- By:
- Publication type:
- Article
32
Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs.
- Published in:
- 2021
- By:
- Publication type:
- journal article
33
Variability in interpretation of low-dose chest CT using computerized assessment in a nationwide lung cancer screening program: comparison of prospective reading at individual institutions and retrospective central reading.
- Published in:
- 2021
- By:
- Publication type:
- journal article
34
Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals.
- Published in:
- European Radiology, 2021, v. 31, n. 2, p. 1069, doi. 10.1007/s00330-020-07219-4
- By:
- Publication type:
- Article
35
Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: comparison with the conventional reading system.
- Published in:
- 2021
- By:
- Publication type:
- journal article
36
Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration.
- Published in:
- European Radiology, 2020, v. 30, n. 12, p. 6902, doi. 10.1007/s00330-020-07062-7
- By:
- Publication type:
- Article
37
Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.
- Published in:
- 2020
- By:
- Publication type:
- journal article
38
Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.
- Published in:
- PLoS ONE, 2016, v. 11, n. 10, p. 1, doi. 10.1371/journal.pone.0164924
- By:
- Publication type:
- Article
39
Methods of Visualizing the Results of an Artificial-Intelligence-Based Computer-Aided Detection System for Chest Radiographs: Effect on the Diagnostic Performance of Radiologists.
- Published in:
- Diagnostics (2075-4418), 2023, v. 13, n. 6, p. 1089, doi. 10.3390/diagnostics13061089
- By:
- Publication type:
- Article
40
Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.
- Published in:
- Diagnostic & Interventional Radiology, 2025, v. 31, n. 2, p. 1, doi. 10.4274/dir.2024.242835
- By:
- Publication type:
- Article
41
Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.
- Published in:
- JAMA Network Open, 2019, p. e191095, doi. 10.1001/jamanetworkopen.2019.1095
- By:
- Publication type:
- Article
42
Determination of the optimum definition of growth evaluation for indeterminate pulmonary nodules detected in lung cancer screening.
- Published in:
- PLoS ONE, 2022, v. 17, n. 9, p. 1, doi. 10.1371/journal.pone.0274583
- By:
- Publication type:
- Article
43
Cone beam computed tomography virtual navigation-guided transthoracic biopsy of small (≤ 1 cm) pulmonary nodules: impact of nodule visibility during real-time fluoroscopy.
- Published in:
- British Journal of Radiology, 2018, v. 91, n. 1087, p. N.PAG, doi. 10.1259/bjr.20170805
- By:
- Publication type:
- Article
44
COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system.
- Published in:
- PLoS ONE, 2021, v. 16, n. 6, p. 1, doi. 10.1371/journal.pone.0252440
- By:
- Publication type:
- Article