Young H Kim

Young H Kim, MD, PhD is a Professor in the Department of Radiology at UMass Chan Medical School's T.H. Chan School of Medicine. He serves in the Division of Abdominal Imaging and specializes in abdominal radiology with particular expertise in ultrasound and MRI applications.

His educational background includes:

• BS Pre-Med from Hanyang University, Seoul
• MS Medicine from Hanyang University, Seoul
• MD from Hanyang University, Seoul
• PhD Medicine from University of Ulsan, Ulsan
• Residency in Radiology at Asan Medical Center, Seoul
• Fellowship in Abdominal Imaging at Massachusetts General Hospital/Harvard University
• Fellowship in Abdominal Imaging and Nonvascular Interventional Radiology

Dr. Kim's research focuses on advancing abdominal imaging techniques with recent emphasis on artificial intelligence applications in medical ultrasonography. His work bridges traditional radiology with emerging deep learning technologies, particularly for gallbladder pathologies, thyroid diagnostics, and emergency department ultrasound applications. He has pioneered studies on AI integration in radiology workflows, examining how large language models like ChatGPT can impact diagnostic performance and medical education.

His publication trends reveal a significant shift toward AI applications since 2021, while maintaining expertise in abdominal imaging fundamentals. Recent work examines deep learning applications in ultrasound for acute gallbladder pathologies (2024), thyroid FNA sample analysis (2023), and emergency department liver function evaluation (2021). Earlier work established foundational knowledge in hepatobiliary MRI, gadoxetate contrast applications, and emergency obstetric ultrasound.

Dr. Kim collaborates extensively with colleagues including Carolyn Dupuis, Alan Goldstein, and Adib Karam across multiple imaging specialties. His work demonstrates consistent translational research from basic imaging techniques to clinical implementation, particularly in emergency settings and AI-enhanced diagnostics.

He maintains active involvement in teaching and clinical practice within the Abdominal Imaging Division, with particular focus on training the next generation of radiologists in both traditional and AI-enhanced imaging techniques. His research laboratory appears focused on medical image analysis, though specific lab details aren't provided in the source material.