Eun-Kyung Kim

Professor

Yonsei University College of Medicine

South Korea

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Dr. Eun-Kyung Kim is a Professor at Yonsei University, College of Medicine, South Korea. Her research focuses on the application of artificial intelligence in medical imaging, particularly in radiology for pediatric and adult cases. Recent publications include studies on AI models for diagnosing conditions like intussusception and pneumothorax, as well as the impact of AI on breast cancer detection and interpretation efficiency.

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2025

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Sungwon Kim

SOUTH KOREA

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Jung Hyun Yoon

Yonsei University

SOUTH KOREA

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Nak-Hoon Son

Assistant Professor

Keimyung University

SOUTH KOREA

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Taeyoung Kyong

Yonsei University College of Medicine

SOUTH KOREA

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Eun-Kyung Kim

Research Interests

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About

Articles (17)

In Vivo Conductivity of Normal Fibroglandular Tissue Using MR Electrical Properties Tomography: A Comparison With Apparent Diffusion Coefficient

External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study

Applications of artificial intelligence-based computer-assisted diagnosis in breast radiology: a narrative review

Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence

Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time

Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography

Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points

Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs

Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs

The impact of artificial intelligence on the reading times of radiologists for chest radiographs

Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level

US, Mammography, and Histopathologic Evaluation to Identify Low Nuclear Grade Ductal Carcinoma in Situ

Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics

Sonographic predictors of aggressive behavior in medullary thyroid carcinomas

Research Highlight: Artificial Intelligence for Ruling Out Negative Examinations in Screening Breast MRI

Successful Implementation of an Artificial Intelligence-Based Computer-Aided Detection System for Chest Radiography in Daily Clinical Practice

Application of artificial intelligence–based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms

Collaborators (9)

Hyun Joo Shin

ja seung koo

Hanpyo Hong

Min Jung Kim

Dong‐Hyun Kim

Sungwon Kim

Jung Hyun Yoon

Nak-Hoon Son

Taeyoung Kyong

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