2015.03~2017.02, Seoul School of Integrated Sciences & Technologies (aSSIST)
Ph.D. majored in Business Administration

2002.03~2004.08, Graduate School of Public Health, Seoul National University
Diploma majored in Health Economics

1999.08~2000.08, Helsinki School of Economics and Business Administration
Master of Business Administration, Outstanding performer

1991.03~1997.02, Sung Kyun Kwan University
Bachelor majored in Genetic Engineering

October 2004 ~ Present, Senior Director, Government Affairs & Market Access, Medtroic Korea
June 1996 ~ October 2004, Reimbursement Manager, Johnson&Johnson Medical Korea

global strategies for medtech market access
global health policies
global health technology assessment
global health economics analysis
global reimbursement and payment system

Novenber 2019 : Innovative Leader Award, Great Place to Work(GPTW) Institute
February 2015 : Award of Minister of Health and Welfare, Ministry of Health and Welfare(MoHW)

[Peer-reviewed Journals]
“Delayed Patient Access to Innovative Medical Technologies in South Korea: A Lead-Time Analysis of Reimbursement Coverage Determinations”, International Journal of Technology Assessment in Health Care, 2019;35(3):229-236
“Moon Care and Preliminary Benefit Program in South Korea: Innovative Strategies in Post-Market Evidence Generation for Medical Devices”, J Health Tech Assess, 2018;6(1):33-42.
Etc.

This presentation provides a comprehensive overview of the U.S. health insurance system, distinguishing between public programs (e.g., Medicare, Medicaid) and private coverage (e.g., employer-sponsored and commercial insurance). It will cover the scope of benefits under each insurance type and explain the structure and administration of these programs. Furthermore, the session will explore key payment models in the U.S. healthcare system—including Fee-for-Service, Bundled Payments, and Value-Based Payment—and highlight recent policy trends. The presentation aims to offer practical insights for global healthcare stakeholders by focusing on structural elements that are relevant for comparative analysis with the Korean healthcare system.

B.S. in Control and Instrumentation Engineering, College of Engineering, Seoul National University
M.S. in Biomedical Engineering, Graduate School, Seoul National University
Ph.D. in Biomedical Engineering, Graduate School, Seoul National University

2016–Present Associate Professor (2016), Professor (2022–present), Department of Biomedical Engineering, University of Ulsan College of Medicine / Institute of Biomedical Engineering, Asan Medical Center
2012–2016 Associate Research Professor, Institute of Biomedical Engineering, Asan Medical Center, Seoul
2007–2012 Research Professor, College of Medicine, Korea University

Surgery robot, Intervention robot, Rehabilitation robot, Mechatronics, Simulation, Medical device safety standard

2020 Korea’s Top 10 Mechanical Technologies of the Year

This presentation explores the evolution and emerging future of surgical robotics through the framework of Physical AI. Physical AI marks a significant paradigm shift from traditional industrial robotics, enabling surgical robots to respond to intraoperative variability such as tissue deformation and unexpected events like bleeding. Looking ahead, the field is expected to evolve toward greater levels of supervised autonomy, enhanced human-robot collaboration, miniaturization, and integration of foundation models such as large language and vision-language models. The advancement of digital healthcare—including AI and robotics—should be viewed as an ongoing iterative process of digital transformation.

Yonsei University College of Medicine
M.D.

Graduate School, Yonsei University College of Medicine
MMSc

Graduate School, Yonsei University College of Medicine
Ph.D.

Mar2010 – Feb 2014 : Resident (Radiology), Department of Radiology, Severance hospital, Yonsei University College of Medicine, Korea
Mar2014 – Feb 2015 : Fellow (Pediatric radiology), Department of Radiology, Severance hospital, Yonsei University College of Medicine, Korea
Mar2015 – Feb 2016 : Clinical research assistant professor (Pediatric radiology), Department of Radiology, Severance hospital, Yonsei University College of Medicine, Korea
Mar 2016-Feb 2021 : Clinical assistant professor (Pediatric radiology), Department of Radiology, Severance hospital, Yonsei University College of Medicine, Korea
Mar 2021-Feb 2023 : Clinical assistant professor (Pediatric radiology), Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Korea
Mar 2023-Feb 2024 : Assistant professor (Pediatric radiology), Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Korea
Mar 2024-present : Associate professor (Pediatric radiology), Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Korea
Mar 2024-Feb 2025 : Visiting scholar, Department of Radiology & AIMI center, Stanford University, USA

Pediatric radiology, quantitative imaging, artificial intelligence

2025.4 2025 Best researcher award, Korean Society of Pediatric Radiology
2024.8 KSR - Education award, Korean Society of Radiology
2024.5 Excellent faculty award, Yongin Severance Hospital
2022.5 Excellent Research Award, Korean Society of Ultrasound in Medicine
2021.6 2021 Best researcher award, Korean Society of Pediatric Radiology
2019.5 Excellent faculty award, Severance Hospital

Song K, Lee E, Lee HS, Lee H, Chae HW, Shin HJ (2025) Development of a simplified prediction model for diagnosing progressive central precocious puberty using clinical and pelvic ultrasound parameters. PLoS One 20:e0323549
Shin HJ, Han K, Son NH et al (2024) Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points. Sci Rep 14:31329
Lee S, Kim EK, Han K, Ryu L, Lee EH, Shin HJ (2024) Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence. Sci Rep 14:19624
Shin HJ, Lee EH, Han K, Ryu L, Kim EK (2024) Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results. Sci Rep 14:14415 Shin HJ, Han K, Ryu L, Kim EK (2023) The impact of artificial intelligence on the reading times of radiologists for chest radiographs. NPJ Digit Med 6:82
Etc.

Artificial intelligence (AI) is being actively researched and introduced into the medical field, particularly in medical imaging. Some AI solutions have already entered the stage of real-world clinical use. This adoption is the result of a convergence of structured large-scale datasets, advancements in AI technologies, and high clinical and diagnostic demands.
This presentation will examine real-world implementation cases to explore the current status of AI integration in clinical practice, as well as the key factors that enable its effective adoption. In particular, it will highlight the impact of human-AI interaction on clinical decision-making and patient care, along with examples of newly discovered clinical value enabled by AI. It will also address several practical challenges and issues encountered in clinical settings, including the gap between industry-driven development and actual clinical needs, limitations in post-deployment optimization, the disconnect between rapid technological advancements and the pace of medical validation, uncertainty surrounding viable business models, algorithmic bias, and questions of legal responsibility.
Ultimately, realizing the clinical value of AI-based medical devices requires more than technological development and deployment. It calls for user-centered feedback systems, continuous performance monitoring, regulatory and institutional stability, and close collaboration between healthcare professionals and the industry. With such a strategic approach, AI can evolve into a true clinical partner in modern healthcare.

1992 – 1998 MD, Seoul National University Medical College
2006 – 2008 PhD, Seoul National University Medical College

2021 – 2022 AI Program Committee Chair, Asian Oceanian Congress of Radiology 2022
2023 – 2024 Secretary General, Korean Society of Thoracic Radiology
2021 – Present Secretary of clinical research, Korean Society of Radiology
2023 – Present Active member of Fleischner Society
2025 – Present President, Korean Society of Artificial Intelligence in Medicine

1. Radiology artificial intelligence (AI): from development to clinical implementation
2. Lung nodules, lung cancer imaging & radiomics
3. Percutaneous lung biopsies and other thoracic interventional procedures

2021 Grand Prize of the Korean Medical Devices Industry Association
2022 KSR-TaeJoon Radiology Award, Korean Society of Radiology
2022 Best Academic Awards, Korean Society of Artificial Intelligence in Medicine
2022 Commendation, Minister of Health and Welfare, Korea
2024 Seoul National University Academic Research and Education Award

260 SCI/SCIE articles & 146 papers as first/corresponding author (H-index = 64)
AI Applications for Thoracic Imaging: Considerations for Best Practice. Radiology. 2025 Feb;314(2):e240650. doi: 10.1148/radiol.240650.
Effect of Human-AI Interaction on Detection of Malignant Lung Nodules on Chest Radiographs. Radiology. 2023 Jun;307(5):e222976. doi: 10.1148/radiol.222976.
AI: Workload Reduction by Autonomous Reporting of Normal Chest Radiographs. Radiology. 2023 May;307(3):e230252. doi: 10.1148/radiol.230252. Epub 2023 Mar 7

This talk shares real-world clinical experiences with AI devices for clinical practices, particularly for radiology, focusing on both development collaboration and practical use in patient care. As a radiologist, I will highlight what worked, what didn’t, and what developers should consider for effective clinical integration.